diff --git a/electron/main.ts b/electron/main.ts
index 34fe45a..f60e80b 100644
--- a/electron/main.ts
+++ b/electron/main.ts
@@ -4145,6 +4145,111 @@ function registerIpcHandlers() {
       return { success: false, error: String(e) }
     }
   })
+
+  ipcMain.handle('ai:getEmbeddingModelProfiles', async () => {
+    try {
+      const { localEmbeddingModelService } = await import('./services/search/embeddingModelService')
+      return {
+        success: true,
+        result: localEmbeddingModelService.listProfiles(),
+        currentProfileId: localEmbeddingModelService.getCurrentProfileId()
+      }
+    } catch (e) {
+      console.error('[AI] 获取语义模型列表失败:', e)
+      return { success: false, error: String(e) }
+    }
+  })
+
+  ipcMain.handle('ai:setEmbeddingModelProfile', async (_, profileId: string) => {
+    try {
+      const { localEmbeddingModelService } = await import('./services/search/embeddingModelService')
+      const result = localEmbeddingModelService.setCurrentProfileId(profileId)
+      return { success: true, result }
+    } catch (e) {
+      console.error('[AI] 设置语义模型失败:', e)
+      return { success: false, error: String(e) }
+    }
+  })
+
+  ipcMain.handle('ai:getEmbeddingDeviceStatus', async () => {
+    try {
+      const { localEmbeddingModelService } = await import('./services/search/embeddingModelService')
+      return {
+        success: true,
+        result: localEmbeddingModelService.getDeviceStatus()
+      }
+    } catch (e) {
+      console.error('[AI] 获取语义向量计算模式失败:', e)
+      return { success: false, error: String(e) }
+    }
+  })
+
+  ipcMain.handle('ai:setEmbeddingDevice', async (_, device: string) => {
+    try {
+      const { localEmbeddingModelService } = await import('./services/search/embeddingModelService')
+      const result = localEmbeddingModelService.setCurrentDevice(device)
+      return {
+        success: true,
+        result,
+        status: localEmbeddingModelService.getDeviceStatus()
+      }
+    } catch (e) {
+      console.error('[AI] 设置语义向量计算模式失败:', e)
+      return { success: false, error: String(e) }
+    }
+  })
+
+  ipcMain.handle('ai:getEmbeddingModelStatus', async (_, profileId?: string) => {
+    try {
+      const { localEmbeddingModelService } = await import('./services/search/embeddingModelService')
+      return {
+        success: true,
+        result: await localEmbeddingModelService.getModelStatus(profileId)
+      }
+    } catch (e) {
+      console.error('[AI] 获取语义模型状态失败:', e)
+      return { success: false, error: String(e) }
+    }
+  })
+
+  ipcMain.handle('ai:downloadEmbeddingModel', async (event, profileId?: string) => {
+    try {
+      const { localEmbeddingModelService } = await import('./services/search/embeddingModelService')
+      const result = await localEmbeddingModelService.downloadModel(profileId, (progress) => {
+        event.sender.send('ai:embeddingModelDownloadProgress', progress)
+      })
+      return { success: true, result }
+    } catch (e) {
+      console.error('[AI] 下载语义模型失败:', e)
+      return { success: false, error: String(e) }
+    }
+  })
+
+  ipcMain.handle('ai:clearEmbeddingModel', async (_, profileId?: string) => {
+    try {
+      const { localEmbeddingModelService } = await import('./services/search/embeddingModelService')
+      return {
+        success: true,
+        result: await localEmbeddingModelService.clearModel(profileId)
+      }
+    } catch (e) {
+      console.error('[AI] 清理语义模型失败:', e)
+      return { success: false, error: String(e) }
+    }
+  })
+
+  ipcMain.handle('ai:clearSemanticVectorIndex', async (_, vectorModel?: string) => {
+    try {
+      const { chatSearchIndexService } = await import('./services/search/chatSearchIndexService')
+      return {
+        success: true,
+        result: chatSearchIndexService.clearSemanticVectorIndex(vectorModel)
+      }
+    } catch (e) {
+      console.error('[AI] 清理语义向量索引失败:', e)
+      return { success: false, error: String(e) }
+    }
+  })
 }
 
 // 主窗口引用
diff --git a/electron/preload.ts b/electron/preload.ts
index 55ae85c..67559e9 100644
--- a/electron/preload.ts
+++ b/electron/preload.ts
@@ -20,6 +20,17 @@ type SessionVectorIndexProgressEvent = {
   vectorModel: string
 }
 
+type EmbeddingModelDownloadProgress = {
+  profileId: string
+  displayName: string
+  remoteHost?: string
+  file?: string
+  loaded?: number
+  total?: number
+  percent?: number
+  status?: string
+}
+
 function getMcpLaunchConfigSafe(): Promise<{
   command: string
   args: string[]
@@ -543,6 +554,14 @@ contextBridge.exposeInMainWorld('electronAPI', {
     getSessionVectorIndexState: (sessionId: string) => ipcRenderer.invoke('ai:getSessionVectorIndexState', sessionId),
     prepareSessionVectorIndex: (options: { sessionId: string }) => ipcRenderer.invoke('ai:prepareSessionVectorIndex', options),
     cancelSessionVectorIndex: (sessionId: string) => ipcRenderer.invoke('ai:cancelSessionVectorIndex', sessionId),
+    getEmbeddingModelProfiles: () => ipcRenderer.invoke('ai:getEmbeddingModelProfiles'),
+    setEmbeddingModelProfile: (profileId: string) => ipcRenderer.invoke('ai:setEmbeddingModelProfile', profileId),
+    getEmbeddingDeviceStatus: () => ipcRenderer.invoke('ai:getEmbeddingDeviceStatus'),
+    setEmbeddingDevice: (device: 'cpu' | 'dml') => ipcRenderer.invoke('ai:setEmbeddingDevice', device),
+    getEmbeddingModelStatus: (profileId?: string) => ipcRenderer.invoke('ai:getEmbeddingModelStatus', profileId),
+    downloadEmbeddingModel: (profileId?: string) => ipcRenderer.invoke('ai:downloadEmbeddingModel', profileId),
+    clearEmbeddingModel: (profileId?: string) => ipcRenderer.invoke('ai:clearEmbeddingModel', profileId),
+    clearSemanticVectorIndex: (vectorModel?: string) => ipcRenderer.invoke('ai:clearSemanticVectorIndex', vectorModel),
     onSummaryChunk: (callback: (chunk: string) => void) => {
       ipcRenderer.on('ai:summaryChunk', (_, chunk) => callback(chunk))
       return () => ipcRenderer.removeAllListeners('ai:summaryChunk')
@@ -558,6 +577,10 @@ contextBridge.exposeInMainWorld('electronAPI', {
     onSessionVectorIndexProgress: (callback: (event: SessionVectorIndexProgressEvent) => void) => {
       ipcRenderer.on('ai:sessionVectorIndexProgress', (_, event) => callback(event))
       return () => ipcRenderer.removeAllListeners('ai:sessionVectorIndexProgress')
+    },
+    onEmbeddingModelDownloadProgress: (callback: (event: EmbeddingModelDownloadProgress) => void) => {
+      ipcRenderer.on('ai:embeddingModelDownloadProgress', (_, event) => callback(event))
+      return () => ipcRenderer.removeAllListeners('ai:embeddingModelDownloadProgress')
     }
   }
 })
diff --git a/electron/services/config.ts b/electron/services/config.ts
index ca199f4..8d736ab 100644
--- a/electron/services/config.ts
+++ b/electron/services/config.ts
@@ -110,6 +110,8 @@ interface ConfigSchema {
   aiEnableCache: boolean
   aiEnableThinking: boolean  // 是否显示思考过程
   aiMessageLimit: number     // 摘要提取的消息条数限制
+  aiEmbeddingModelProfile: string
+  aiEmbeddingDevice: 'cpu' | 'dml'
   mcpEnabled: boolean
   mcpExposeMediaPaths: boolean
   mcpProxyPort: number
@@ -172,6 +174,8 @@ const defaults: ConfigSchema = {
   aiEnableCache: true,
   aiEnableThinking: true,  // 默认显示思考过程
   aiMessageLimit: 3000,    // 默认3000条，用户可调至5000
+  aiEmbeddingModelProfile: 'bge-large-zh-v1.5-int8',
+  aiEmbeddingDevice: 'cpu',
   mcpEnabled: false,
   mcpExposeMediaPaths: true,
   mcpProxyPort: 5032,
diff --git a/electron/services/search/chatSearchIndexService.ts b/electron/services/search/chatSearchIndexService.ts
index 1ef514a..b369ee5 100644
--- a/electron/services/search/chatSearchIndexService.ts
+++ b/electron/services/search/chatSearchIndexService.ts
@@ -3,6 +3,11 @@ import { existsSync, mkdirSync } from 'fs'
 import { join } from 'path'
 import { chatService, type Message } from '../chatService'
 import { ConfigService } from '../config'
+import {
+  float32ArrayToBuffer,
+  hashEmbeddingContent,
+  localEmbeddingModelService
+} from './embeddingModelService'
 
 export type ChatSearchIndexProgressStage =
   | 'preparing_index'
@@ -56,6 +61,7 @@ export interface ChatSearchSessionResult {
 
 export type ChatVectorIndexProgressStage =
   | 'preparing'
+  | 'downloading_model'
   | 'indexing_messages'
   | 'vectorizing_messages'
   | 'completed'
@@ -84,6 +90,9 @@ export interface ChatVectorIndexState {
   isVectorComplete: boolean
   isVectorRunning: boolean
   vectorModel: string
+  vectorModelName?: string
+  vectorProviderAvailable?: boolean
+  vectorProviderError?: string
 }
 
 export interface ChatVectorSearchSessionResult {
@@ -112,17 +121,7 @@ type MessageIndexRow = {
 }
 
 type MessageVectorRow = MessageIndexRow & {
-  vector_json: string
-}
-
-type SparseVector = Array<[number, number]>
-
-interface LocalVectorProvider {
-  id: string
-  buildVector(text: string): SparseVector
-  parseVector(value: string): SparseVector
-  toWeightMap(vector: SparseVector): Map<number, number>
-  dot(queryWeights: Map<number, number>, vector: SparseVector): number
+  distance: number
 }
 
 type SessionVectorStateRow = {
@@ -141,81 +140,18 @@ type VectorTask = {
 }
 
 const INDEX_DB_NAME = 'chat_search_index.db'
-const INDEX_SCHEMA_VERSION = '1'
+const INDEX_SCHEMA_VERSION = '3'
 const INDEX_BATCH_SIZE = 800
 const MAX_INDEX_TEXT_CHARS = 8000
 const MAX_EXCERPT_RADIUS = 48
 const MAX_INDEX_SEARCH_CANDIDATES = 240
-const VECTOR_MODEL_ID = 'local-chargram-hash-v1'
-const VECTOR_DIMENSIONS = 2048
-const VECTOR_BATCH_SIZE = 800
-const MAX_VECTOR_TEXT_CHARS = 2400
-const MAX_VECTOR_SCAN_ROWS = 120000
-const VECTOR_MIN_SCORE = 0.055
+const VECTOR_BATCH_SIZE = 32
+const VECTOR_SEARCH_OVERFETCH = 8
+const VECTOR_MIN_SCORE = 0.45
 // Vector hits are recall supplements, so keep them below high-confidence keyword hits.
 const VECTOR_SCORE_BASE = 560
 const VECTOR_SCORE_SCALE = 420
 const VECTOR_INDEX_CANCELLED_ERROR = 'VECTOR_INDEX_CANCELLED'
-const VECTOR_STOP_PHRASES = [
-  '有没有',
-  '是不是',
-  '是否',
-  '什么',
-  '哪个',
-  '哪些',
-  '什么时候',
-  '为什么',
-  '怎么',
-  '如何',
-  '帮我',
-  '看看',
-  '请问',
-  '问一下',
-  '聊天记录',
-  '聊天',
-  '消息',
-  '记录',
-  '内容',
-  '这个',
-  '那个',
-  '我们',
-  '他们',
-  '对方',
-  '最近',
-  '说过',
-  '提到',
-  '关于',
-  '一下'
-]
-const VECTOR_STOP_WORDS = new Set([
-  'the',
-  'and',
-  'for',
-  'with',
-  'that',
-  'this',
-  'what',
-  'when',
-  'where',
-  'which',
-  'why',
-  'how',
-  'have',
-  'has',
-  '是否',
-  '什么',
-  '哪个',
-  '哪些',
-  '怎么',
-  '如何',
-  '我们',
-  '他们',
-  '对方',
-  '消息',
-  '聊天',
-  '记录',
-  '内容'
-])
 
 function cursorKey(message: Pick<Message, 'localId' | 'createTime' | 'sortSeq'>): string {
   return `${Number(message.localId || 0)}:${Number(message.createTime || 0)}:${Number(message.sortSeq || 0)}`
@@ -239,6 +175,15 @@ function compareIndexRowCursorAsc(
     || Number(a.local_id || 0) - Number(b.local_id || 0)
 }
 
+function vectorSessionKey(sessionId: string): number {
+  let hash = 2166136261
+  for (let index = 0; index < sessionId.length; index += 1) {
+    hash ^= sessionId.charCodeAt(index)
+    hash = Math.imul(hash, 16777619)
+  }
+  return hash >>> 0
+}
+
 function normalizeSearchText(value?: string): string {
   return String(value || '')
     .toLowerCase()
@@ -338,128 +283,6 @@ function buildSearchTokens(value: string): string {
   return uniqueStrings(tokens).join(' ')
 }
 
-function normalizeVectorText(value: string): string {
-  let normalized = normalizeSearchText(value).slice(0, MAX_VECTOR_TEXT_CHARS)
-
-  for (const phrase of VECTOR_STOP_PHRASES) {
-    normalized = normalized.replace(new RegExp(phrase, 'gi'), ' ')
-  }
-
-  return normalized.replace(/\s+/g, ' ').trim()
-}
-
-function hashString(value: string): number {
-  let hash = 2166136261
-  for (let index = 0; index < value.length; index += 1) {
-    hash ^= value.charCodeAt(index)
-    hash = Math.imul(hash, 16777619)
-  }
-  return hash >>> 0
-}
-
-function addVectorFeature(weights: Map<number, number>, feature: string, weight: number): void {
-  if (!feature || VECTOR_STOP_WORDS.has(feature)) return
-
-  const hash = hashString(feature)
-  const dimension = hash % VECTOR_DIMENSIONS
-  const signedWeight = (hash & 0x80000000) ? -weight : weight
-  weights.set(dimension, (weights.get(dimension) || 0) + signedWeight)
-}
-
-function addChineseVectorFeatures(weights: Map<number, number>, segment: string): void {
-  if (!segment || VECTOR_STOP_WORDS.has(segment)) return
-
-  if (segment.length >= 2 && segment.length <= 12) {
-    addVectorFeature(weights, `zh:${segment}`, 1.35)
-  }
-
-  for (let size = 2; size <= 4; size += 1) {
-    if (segment.length < size) continue
-    const weight = size === 2 ? 0.9 : size === 3 ? 1.1 : 0.85
-    for (let index = 0; index <= segment.length - size; index += 1) {
-      const gram = segment.slice(index, index + size)
-      if (VECTOR_STOP_WORDS.has(gram)) continue
-      addVectorFeature(weights, `c${size}:${gram}`, weight)
-    }
-  }
-}
-
-function buildLocalSearchVector(value: string): SparseVector {
-  const normalized = normalizeVectorText(value)
-  if (!normalized) return []
-
-  const weights = new Map<number, number>()
-  const latinWords: string[] = []
-
-  for (const match of normalized.matchAll(/[\u3400-\u9fff]+/g)) {
-    addChineseVectorFeatures(weights, match[0])
-  }
-
-  for (const match of normalized.matchAll(/[a-z0-9_@.\-]{2,}/g)) {
-    const word = match[0]
-    if (VECTOR_STOP_WORDS.has(word)) continue
-    latinWords.push(word)
-    addVectorFeature(weights, `w:${word}`, 1.2)
-  }
-
-  for (let index = 0; index < latinWords.length - 1; index += 1) {
-    addVectorFeature(weights, `wb:${latinWords[index]} ${latinWords[index + 1]}`, 1.35)
-  }
-
-  let norm = 0
-  for (const weight of weights.values()) {
-    norm += weight * weight
-  }
-
-  if (norm <= 0) return []
-
-  const scale = Math.sqrt(norm)
-  return Array.from(weights.entries())
-    .map(([dimension, weight]) => [dimension, Number((weight / scale).toFixed(6))] as [number, number])
-    .filter(([, weight]) => Math.abs(weight) > 0.000001)
-    .sort((a, b) => a[0] - b[0])
-}
-
-function parseSparseVector(value: string): SparseVector {
-  try {
-    const parsed = JSON.parse(value)
-    if (!Array.isArray(parsed)) return []
-
-    const vector: SparseVector = []
-    for (const item of parsed) {
-      if (!Array.isArray(item) || item.length < 2) continue
-      const dimension = Number(item[0])
-      const weight = Number(item[1])
-      if (!Number.isInteger(dimension) || dimension < 0 || !Number.isFinite(weight)) continue
-      vector.push([dimension, weight])
-    }
-    return vector
-  } catch {
-    return []
-  }
-}
-
-function dotSparseVector(queryWeights: Map<number, number>, vector: SparseVector): number {
-  let score = 0
-  for (const [dimension, weight] of vector) {
-    const queryWeight = queryWeights.get(dimension)
-    if (queryWeight) score += queryWeight * weight
-  }
-  return score
-}
-
-function sparseVectorToMap(vector: SparseVector): Map<number, number> {
-  return new Map(vector)
-}
-
-const localVectorProvider: LocalVectorProvider = {
-  id: VECTOR_MODEL_ID,
-  buildVector: buildLocalSearchVector,
-  parseVector: parseSparseVector,
-  toWeightMap: sparseVectorToMap,
-  dot: dotSparseVector
-}
-
 function createVectorExcerpt(row: Pick<MessageIndexRow, 'parsed_content' | 'search_text'>, query: string): string {
   const text = String(row.parsed_content || row.search_text || '')
   if (!text) return ''
@@ -629,6 +452,8 @@ export class ChatSearchIndexService {
   private db: Database.Database | null = null
   private dbPath: string | null = null
   private vectorTasks = new Map<string, VectorTask>()
+  private sqliteVectorAvailable = false
+  private sqliteVectorError = ''
 
   private getCacheBasePath(): string {
     const configService = new ConfigService()
@@ -662,10 +487,24 @@ export class ChatSearchIndexService {
     const db = new Database(nextDbPath)
     this.db = db
     this.dbPath = nextDbPath
+    this.loadSqliteVectorExtension(db)
     this.ensureSchema(db)
     return db
   }
 
+  private loadSqliteVectorExtension(db: Database.Database): void {
+    try {
+      const sqliteVec = require('sqlite-vec') as { load: (db: Database.Database) => void }
+      sqliteVec.load(db)
+      this.sqliteVectorAvailable = true
+      this.sqliteVectorError = ''
+    } catch (error) {
+      this.sqliteVectorAvailable = false
+      this.sqliteVectorError = String(error)
+      console.warn('[ChatSearchIndex] sqlite-vec 加载失败，语义向量检索将降级为关键词检索:', error)
+    }
+  }
+
   private ensureSchema(db: Database.Database): void {
     db.pragma('journal_mode = WAL')
     db.pragma('synchronous = NORMAL')
@@ -720,12 +559,15 @@ export class ChatSearchIndexService {
       );
 
       CREATE TABLE IF NOT EXISTS message_vector_index (
-        message_id INTEGER PRIMARY KEY,
+        id INTEGER PRIMARY KEY AUTOINCREMENT,
+        message_id INTEGER NOT NULL,
         session_id TEXT NOT NULL,
         vector_model TEXT NOT NULL,
-        vector_json TEXT NOT NULL,
-        feature_count INTEGER NOT NULL DEFAULT 0,
-        indexed_at INTEGER NOT NULL
+        embedding_blob BLOB NOT NULL,
+        dim INTEGER NOT NULL,
+        content_hash TEXT NOT NULL,
+        indexed_at INTEGER NOT NULL,
+        UNIQUE(message_id, vector_model)
       );
 
       CREATE TABLE IF NOT EXISTS session_vector_state (
@@ -745,16 +587,32 @@ export class ChatSearchIndexService {
         ON message_index(session_id, sender_username);
       CREATE INDEX IF NOT EXISTS idx_message_vector_session_model
         ON message_vector_index(session_id, vector_model);
+      CREATE INDEX IF NOT EXISTS idx_message_vector_message_model
+        ON message_vector_index(message_id, vector_model);
       CREATE INDEX IF NOT EXISTS idx_session_vector_state_session
         ON session_vector_state(session_id);
     `)
 
+    if (this.sqliteVectorAvailable) {
+      const dim = localEmbeddingModelService.getProfile().dim
+      db.exec(`
+        CREATE VIRTUAL TABLE IF NOT EXISTS message_embedding_vec USING vec0(
+          vector_id INTEGER PRIMARY KEY,
+          session_key INTEGER PARTITION KEY,
+          session_id TEXT,
+          vector_model TEXT,
+          embedding FLOAT[${dim}]
+        );
+      `)
+    }
+
     db.prepare('INSERT OR REPLACE INTO meta(key, value) VALUES (?, ?)').run('schema_version', INDEX_SCHEMA_VERSION)
   }
 
   private resetSchema(db: Database.Database): void {
     db.exec(`
       DROP TABLE IF EXISTS message_index_fts;
+      DROP TABLE IF EXISTS message_embedding_vec;
       DROP TABLE IF EXISTS message_vector_index;
       DROP TABLE IF EXISTS session_vector_state;
       DROP TABLE IF EXISTS message_index;
@@ -783,26 +641,36 @@ export class ChatSearchIndexService {
     return Number(row?.count || 0)
   }
 
+  private getCurrentVectorProfile() {
+    return localEmbeddingModelService.getProfile()
+  }
+
+  private getCurrentVectorModelId(): string {
+    return this.getCurrentVectorProfile().id
+  }
+
   private getVectorizedCount(db: Database.Database, sessionId: string): number {
+    const vectorModel = this.getCurrentVectorModelId()
     const row = db.prepare(`
       SELECT COUNT(*) AS count
       FROM message_index m
       JOIN message_vector_index v ON v.message_id = m.id
       WHERE m.session_id = ? AND v.vector_model = ?
-    `).get(sessionId, localVectorProvider.id) as { count?: number }
+    `).get(sessionId, vectorModel) as { count?: number }
     return Number(row?.count || 0)
   }
 
   private getVectorTaskKey(sessionId: string): string {
-    return `${sessionId}:${localVectorProvider.id}`
+    return `${sessionId}:${this.getCurrentVectorModelId()}`
   }
 
   private getVectorStateRow(db: Database.Database, sessionId: string): SessionVectorStateRow | null {
+    const vectorModel = this.getCurrentVectorModelId()
     const row = db.prepare(`
       SELECT *
       FROM session_vector_state
       WHERE session_id = ? AND vector_model = ?
-    `).get(sessionId, localVectorProvider.id) as SessionVectorStateRow | undefined
+    `).get(sessionId, vectorModel) as SessionVectorStateRow | undefined
     return row || null
   }
 
@@ -836,7 +704,7 @@ export class ChatSearchIndexService {
         last_error = excluded.last_error
     `).run(
       input.sessionId,
-      localVectorProvider.id,
+      this.getCurrentVectorModelId(),
       input.confirmedAt ?? null,
       input.completedAt ?? null,
       now,
@@ -847,11 +715,13 @@ export class ChatSearchIndexService {
 
   getSessionVectorIndexState(sessionId: string): ChatVectorIndexState {
     const db = this.getDb()
+    const profile = this.getCurrentVectorProfile()
     const indexedCount = this.getIndexedCount(db, sessionId)
     const vectorizedCount = this.getVectorizedCount(db, sessionId)
     const isRunning = this.vectorTasks.has(this.getVectorTaskKey(sessionId))
     const row = this.getVectorStateRow(db, sessionId)
-    const isComplete = Number(row?.is_complete || 0) === 1
+    const isComplete = this.sqliteVectorAvailable
+      && Number(row?.is_complete || 0) === 1
       && vectorizedCount >= indexedCount
 
     return {
@@ -861,7 +731,10 @@ export class ChatSearchIndexService {
       pendingCount: Math.max(0, indexedCount - vectorizedCount),
       isVectorComplete: isComplete,
       isVectorRunning: isRunning,
-      vectorModel: localVectorProvider.id
+      vectorModel: profile.id,
+      vectorModelName: profile.displayName,
+      vectorProviderAvailable: this.sqliteVectorAvailable,
+      vectorProviderError: this.sqliteVectorError
     }
   }
 
@@ -873,56 +746,78 @@ export class ChatSearchIndexService {
     progress: Omit<ChatVectorIndexProgress, 'vectorModel'>,
     onProgress?: (progress: ChatVectorIndexProgress) => void | Promise<void>
   ): Promise<void> {
+    const profile = this.getCurrentVectorProfile()
     await onProgress?.({
       ...progress,
-      vectorModel: localVectorProvider.id
+      vectorModel: profile.id
     })
   }
 
-  private upsertVectorRows(
+  private async upsertVectorRows(
     db: Database.Database,
     rows: Array<Pick<MessageIndexRow, 'id' | 'session_id' | 'search_text'> & { indexed_at?: number }>
-  ): void {
+  ): Promise<void> {
     if (rows.length === 0) return
+    if (!this.sqliteVectorAvailable) {
+      throw new Error(`本地语义检索不可用：${this.sqliteVectorError || 'sqlite-vec 未加载'}`)
+    }
+
+    const profile = this.getCurrentVectorProfile()
+    const embeddings = await localEmbeddingModelService.embedTexts(rows.map((row) => row.search_text), profile.id)
 
     const upsertVector = db.prepare(`
       INSERT INTO message_vector_index (
         message_id,
         session_id,
         vector_model,
-        vector_json,
-        feature_count,
+        embedding_blob,
+        dim,
+        content_hash,
         indexed_at
-      ) VALUES (?, ?, ?, ?, ?, ?)
-      ON CONFLICT(message_id) DO UPDATE SET
+      ) VALUES (?, ?, ?, ?, ?, ?, ?)
+      ON CONFLICT(message_id, vector_model) DO UPDATE SET
         session_id = excluded.session_id,
-        vector_model = excluded.vector_model,
-        vector_json = excluded.vector_json,
-        feature_count = excluded.feature_count,
+        embedding_blob = excluded.embedding_blob,
+        dim = excluded.dim,
+        content_hash = excluded.content_hash,
         indexed_at = excluded.indexed_at
     `)
+    const selectVectorId = db.prepare(`
+      SELECT id FROM message_vector_index
+      WHERE message_id = ? AND vector_model = ?
+    `)
+    const upsertVec = db.prepare(`
+      INSERT OR REPLACE INTO message_embedding_vec(vector_id, session_key, session_id, vector_model, embedding)
+      VALUES (?, ?, ?, ?, ?)
+    `)
 
     const run = db.transaction((items: Array<Pick<MessageIndexRow, 'id' | 'session_id' | 'search_text'> & { indexed_at?: number }>) => {
       const now = Date.now()
-      for (const row of items) {
-        const vector = localVectorProvider.buildVector(row.search_text)
+      for (let index = 0; index < items.length; index += 1) {
+        const row = items[index]
+        const vector = embeddings[index]
         upsertVector.run(
           row.id,
           row.session_id,
-          localVectorProvider.id,
-          JSON.stringify(vector),
+          profile.id,
+          float32ArrayToBuffer(vector),
           vector.length,
+          hashEmbeddingContent(row.search_text),
           row.indexed_at || now
         )
+        const vectorRow = selectVectorId.get(row.id, profile.id) as { id?: number } | undefined
+        if (vectorRow?.id) {
+          upsertVec.run(vectorRow.id, vectorSessionKey(row.session_id), row.session_id, profile.id, float32ArrayToBuffer(vector))
+        }
       }
     })
 
     run(rows)
   }
 
-  private upsertMessages(db: Database.Database, sessionId: string, messages: Message[], options: {
+  private async upsertMessages(db: Database.Database, sessionId: string, messages: Message[], options: {
     vectorize?: boolean
-  } = {}): void {
+  } = {}): Promise<void> {
     if (messages.length === 0) return
 
     const upsert = db.prepare(`
@@ -978,22 +873,7 @@ export class ChatSearchIndexService {
       INSERT INTO message_index_fts(rowid, session_id, cursor_key, search_text, token_text)
       VALUES (?, ?, ?, ?, ?)
     `)
-    const upsertVector = db.prepare(`
-      INSERT INTO message_vector_index (
-        message_id,
-        session_id,
-        vector_model,
-        vector_json,
-        feature_count,
-        indexed_at
-      ) VALUES (?, ?, ?, ?, ?, ?)
-      ON CONFLICT(message_id) DO UPDATE SET
-        session_id = excluded.session_id,
-        vector_model = excluded.vector_model,
-        vector_json = excluded.vector_json,
-        feature_count = excluded.feature_count,
-        indexed_at = excluded.indexed_at
-    `)
+    const vectorRows: Array<Pick<MessageIndexRow, 'id' | 'session_id' | 'search_text'> & { indexed_at?: number }> = []
 
     const run = db.transaction((items: Message[]) => {
       const indexedAt = Date.now()
@@ -1024,20 +904,29 @@ export class ChatSearchIndexService {
         deleteFts.run(row.id)
         insertFts.run(row.id, sessionId, cursorKey(message), searchText, tokenText)
         if (options.vectorize) {
-          const vector = localVectorProvider.buildVector(searchText)
-          upsertVector.run(
-            row.id,
-            sessionId,
-            localVectorProvider.id,
-            JSON.stringify(vector),
-            vector.length,
-            indexedAt
-          )
+          vectorRows.push({
+            id: row.id,
+            session_id: sessionId,
+            search_text: searchText,
+            indexed_at: indexedAt
+          })
         }
       }
     })
 
     run(messages)
+    if (vectorRows.length > 0) {
+      try {
+        await this.upsertVectorRows(db, vectorRows)
+      } catch (error) {
+        this.setSessionVectorState(db, {
+          sessionId,
+          completedAt: null,
+          isComplete: false,
+          lastError: String(error)
+        })
+      }
+    }
   }
 
   private updateSessionState(db: Database.Database, sessionId: string, newest: Message | null, isComplete: boolean): ChatSearchIndexState {
@@ -1149,7 +1038,7 @@ export class ChatSearchIndexService {
 
         const messages = result.messages || []
         if (messages.length === 0) break
-        this.upsertMessages(db, sessionId, messages, { vectorize: vectorizeDuringIndexing })
+        await this.upsertMessages(db, sessionId, messages, { vectorize: vectorizeDuringIndexing })
         scanned += messages.length
         newest = messages[messages.length - 1] || newest
         cursor = {
@@ -1181,6 +1070,12 @@ export class ChatSearchIndexService {
     }
 
     db.prepare('DELETE FROM message_index_fts WHERE session_id = ?').run(sessionId)
+    if (this.sqliteVectorAvailable) {
+      db.prepare(`
+        DELETE FROM message_embedding_vec
+        WHERE vector_id IN (SELECT id FROM message_vector_index WHERE session_id = ?)
+      `).run(sessionId)
+    }
     db.prepare('DELETE FROM message_vector_index WHERE session_id = ?').run(sessionId)
     db.prepare('DELETE FROM message_index WHERE session_id = ?').run(sessionId)
     db.prepare('DELETE FROM session_index_state WHERE session_id = ?').run(sessionId)
@@ -1193,7 +1088,7 @@ export class ChatSearchIndexService {
     let messages = firstPage.messages || []
     let hasMore = Boolean(firstPage.hasMore)
     if (messages.length > 0) {
-      this.upsertMessages(db, sessionId, messages, { vectorize: vectorizeDuringIndexing })
+      await this.upsertMessages(db, sessionId, messages, { vectorize: vectorizeDuringIndexing })
       scanned += messages.length
       newest = messages[messages.length - 1]
       await this.report({
@@ -1220,7 +1115,7 @@ export class ChatSearchIndexService {
 
       messages = result.messages || []
       if (messages.length === 0) break
-      this.upsertMessages(db, sessionId, messages, { vectorize: vectorizeDuringIndexing })
+      await this.upsertMessages(db, sessionId, messages, { vectorize: vectorizeDuringIndexing })
       scanned += messages.length
       hasMore = Boolean(result.hasMore)
 
@@ -1284,6 +1179,21 @@ export class ChatSearchIndexService {
     return this.getSessionVectorIndexState(sessionId)
   }
 
+  clearSemanticVectorIndex(vectorModel = this.getCurrentVectorModelId()): { success: boolean; deletedCount: number; vectorModel: string } {
+    const db = this.getDb()
+    const row = db.prepare('SELECT COUNT(*) AS count FROM message_vector_index WHERE vector_model = ?').get(vectorModel) as { count?: number }
+    if (this.sqliteVectorAvailable) {
+      db.prepare('DELETE FROM message_embedding_vec WHERE vector_model = ?').run(vectorModel)
+    }
+    db.prepare('DELETE FROM message_vector_index WHERE vector_model = ?').run(vectorModel)
+    db.prepare('DELETE FROM session_vector_state WHERE vector_model = ?').run(vectorModel)
+    return {
+      success: true,
+      deletedCount: Number(row?.count || 0),
+      vectorModel
+    }
+  }
+
   private async runPrepareSessionVectorIndex(
     sessionId: string,
     task: VectorTask,
@@ -1301,6 +1211,35 @@ export class ChatSearchIndexService {
     }, onProgress)
 
     try {
+      if (!this.sqliteVectorAvailable) {
+        throw new Error(`本地语义检索不可用：${this.sqliteVectorError || 'sqlite-vec 未加载'}`)
+      }
+
+      const profile = this.getCurrentVectorProfile()
+      const modelStatus = await localEmbeddingModelService.getModelStatus(profile.id)
+      if (!modelStatus.exists) {
+        await this.reportVectorProgress({
+          sessionId,
+          stage: 'downloading_model',
+          status: 'running',
+          processedCount: 0,
+          totalCount: 0,
+          message: `正在下载本地语义模型：${profile.displayName}`
+        }, onProgress)
+        await localEmbeddingModelService.downloadModel(profile.id, async (progress) => {
+          await this.reportVectorProgress({
+            sessionId,
+            stage: 'downloading_model',
+            status: 'running',
+            processedCount: progress.loaded || 0,
+            totalCount: progress.total || 0,
+            message: progress.percent !== undefined
+              ? `正在下载 ${profile.displayName}：${progress.percent}%`
+              : `正在下载 ${profile.displayName}`
+          }, onProgress)
+        })
+      }
+
       const searchState = await this.ensureSessionIndexed(sessionId, async (progress) => {
         if (task.cancelRequested) {
           throw new Error(VECTOR_INDEX_CANCELLED_ERROR)
@@ -1326,7 +1265,7 @@ export class ChatSearchIndexService {
           status: 'completed',
           processedCount: currentState.vectorizedCount,
           totalCount: currentState.indexedCount,
-          message: `本地向量索引已就绪，共 ${currentState.vectorizedCount} 条消息`
+          message: `本地语义向量索引已就绪，共 ${currentState.vectorizedCount} 条消息`
         }, onProgress)
         return currentState
       }
@@ -1386,11 +1325,11 @@ export class ChatSearchIndexService {
           WHERE m.session_id = ? AND v.message_id IS NULL
           ORDER BY m.id ASC
           LIMIT ?
-        `).all(localVectorProvider.id, sessionId, VECTOR_BATCH_SIZE) as Array<Pick<MessageIndexRow, 'id' | 'session_id' | 'search_text'> & { indexed_at?: number }>
+        `).all(this.getCurrentVectorModelId(), sessionId, VECTOR_BATCH_SIZE) as Array<Pick<MessageIndexRow, 'id' | 'session_id' | 'search_text'> & { indexed_at?: number }>
 
         if (rows.length === 0) break
 
-        this.upsertVectorRows(db, rows)
+        await this.upsertVectorRows(db, rows)
         currentState = this.getSessionVectorIndexState(sessionId)
         await this.reportVectorProgress({
           sessionId,
@@ -1417,7 +1356,7 @@ export class ChatSearchIndexService {
         status: 'completed',
         processedCount: currentState.vectorizedCount,
         totalCount: currentState.indexedCount,
-        message: `本地向量索引已完成，共 ${currentState.vectorizedCount} 条消息`
+        message: `本地语义向量索引已完成，共 ${currentState.vectorizedCount} 条消息`
       }, onProgress)
 
       return currentState
@@ -1576,23 +1515,26 @@ export class ChatSearchIndexService {
     const db = this.getDb()
     const state = await this.ensureSessionIndexed(options.sessionId, options.onProgress)
     const vectorState = this.getSessionVectorIndexState(options.sessionId)
-    const queryVector = localVectorProvider.buildVector(options.query)
+    const profile = this.getCurrentVectorProfile()
     const vectorizedCount = vectorState.vectorizedCount
 
-    if (!vectorState.isVectorComplete || queryVector.length === 0) {
+    if (!this.sqliteVectorAvailable || !vectorState.isVectorComplete || !normalizeSearchText(options.query)) {
       return {
         hits: [],
         indexedCount: state.indexedCount,
         vectorizedCount,
         truncated: false,
-        model: localVectorProvider.id
+        model: profile.id
       }
     }
 
+    const queryVector = await localEmbeddingModelService.embedText(options.query, profile.id)
+    const queryEmbedding = float32ArrayToBuffer(queryVector)
+
     await this.report({
       stage: 'searching_index',
       sessionId: options.sessionId,
-      message: `正在进行本地向量检索：${options.query}`,
+      message: `正在进行本地语义检索：${options.query}`,
       indexedCount: state.indexedCount
     }, options.onProgress)
 
@@ -1600,14 +1542,19 @@ export class ChatSearchIndexService {
     const endTime = toTimestampSeconds(options.endTimeMs)
     const senderUsername = normalizeSearchText(options.senderUsername)
     const direction = options.direction
-    const scanLimit = MAX_VECTOR_SCAN_ROWS
+    const scanLimit = Math.max(options.limit * VECTOR_SEARCH_OVERFETCH, options.limit + 20)
     const sqlFilters: string[] = [
-      'v.session_id = @sessionId',
-      'v.vector_model = @vectorModel'
+      'vec.embedding MATCH @queryEmbedding',
+      'vec.session_key = @sessionKey',
+      'vec.session_id = @sessionId',
+      'vec.vector_model = @vectorModel',
+      'k = @scanLimit'
     ]
     const params: Record<string, unknown> = {
       sessionId: options.sessionId,
-      vectorModel: localVectorProvider.id,
+      sessionKey: vectorSessionKey(options.sessionId),
+      vectorModel: profile.id,
+      queryEmbedding,
       scanLimit: scanLimit + 1
     }
 
@@ -1628,18 +1575,16 @@ export class ChatSearchIndexService {
     }
 
     const rows = db.prepare(`
-      SELECT m.*, v.vector_json
-      FROM message_vector_index v
+      SELECT m.*, vec.distance
+      FROM message_embedding_vec vec
+      JOIN message_vector_index v ON v.id = vec.vector_id
       JOIN message_index m ON m.id = v.message_id
       WHERE ${sqlFilters.join(' AND ')}
-        AND v.feature_count > 0
-      ORDER BY m.sort_seq DESC, m.create_time DESC, m.local_id DESC
-      LIMIT @scanLimit
+      ORDER BY vec.distance ASC
     `).all(params) as MessageVectorRow[]
-    const queryWeights = localVectorProvider.toWeightMap(queryVector)
     const scored = rows
       .map((row) => {
-        const vectorScore = localVectorProvider.dot(queryWeights, localVectorProvider.parseVector(row.vector_json))
+        const vectorScore = Math.max(0, Math.min(1, 1 - Number(row.distance || 0)))
         return {
           row,
           vectorScore
@@ -1662,7 +1607,7 @@ export class ChatSearchIndexService {
       indexedCount: state.indexedCount,
       vectorizedCount,
       truncated: rows.length > scanLimit,
-      model: localVectorProvider.id
+      model: profile.id
     }
   }
 }
diff --git a/electron/services/search/embeddingModelService.ts b/electron/services/search/embeddingModelService.ts
new file mode 100644
index 0000000..39db932
--- /dev/null
+++ b/electron/services/search/embeddingModelService.ts
@@ -0,0 +1,596 @@
+import { createHash } from 'crypto'
+import { existsSync, mkdirSync, readdirSync, rmSync, statSync } from 'fs'
+import { dirname, join } from 'path'
+import { ConfigService } from '../config'
+
+export type EmbeddingModelProfileId =
+  | 'bge-large-zh-v1.5-int8'
+  | 'bge-large-zh-v1.5-fp32'
+  | 'bge-m3'
+
+export type EmbeddingDevice = 'cpu' | 'dml'
+
+export type EmbeddingDeviceStatus = {
+  currentDevice: EmbeddingDevice
+  effectiveDevice: EmbeddingDevice
+  gpuAvailable: boolean
+  provider: 'CPU' | 'DirectML'
+  info: string
+}
+
+export type EmbeddingModelStatus = {
+  profileId: string
+  displayName: string
+  modelId: string
+  dim: number
+  dtype: string
+  sizeLabel: string
+  enabled: boolean
+  exists: boolean
+  modelDir: string
+  sizeBytes: number
+}
+
+export type EmbeddingDownloadProgress = {
+  profileId: string
+  displayName: string
+  remoteHost?: string
+  file?: string
+  loaded?: number
+  total?: number
+  percent?: number
+  status?: string
+}
+
+export type EmbeddingModelProfile = {
+  id: EmbeddingModelProfileId
+  displayName: string
+  description: string
+  modelId: string
+  remoteHosts: string[]
+  remotePathTemplate: string
+  revision: string
+  dim: number
+  maxTokens: number
+  maxTextChars: number
+  dtype: 'q8' | 'fp32'
+  sizeLabel: string
+  enabled: boolean
+}
+
+const MODELSCOPE_HOST = 'https://www.modelscope.cn/'
+const MODELSCOPE_PATH_TEMPLATE = 'models/{model}/resolve/{revision}/'
+const MODELSCOPE_REVISION = 'master'
+
+export const DEFAULT_EMBEDDING_MODEL_PROFILE: EmbeddingModelProfileId = 'bge-large-zh-v1.5-int8'
+
+const EMBEDDING_MODEL_PROFILES: EmbeddingModelProfile[] = [
+  {
+    id: 'bge-large-zh-v1.5-int8',
+    displayName: 'BGE Large 中文 · 推荐',
+    description: '默认档位，1024 维中文语义向量，优先兼顾召回质量和本地 CPU 性能。',
+    modelId: 'Xenova/bge-large-zh-v1.5',
+    remoteHosts: [MODELSCOPE_HOST],
+    remotePathTemplate: MODELSCOPE_PATH_TEMPLATE,
+    revision: MODELSCOPE_REVISION,
+    dim: 1024,
+    maxTokens: 512,
+    maxTextChars: 480,
+    dtype: 'q8',
+    sizeLabel: '约 330 MB',
+    enabled: true
+  },
+  {
+    id: 'bge-large-zh-v1.5-fp32',
+    displayName: 'BGE Large 中文 · 高质量',
+    description: '同模型 FP32 推理，精度更完整，下载和内存占用更高。',
+    modelId: 'Xenova/bge-large-zh-v1.5',
+    remoteHosts: [MODELSCOPE_HOST],
+    remotePathTemplate: MODELSCOPE_PATH_TEMPLATE,
+    revision: MODELSCOPE_REVISION,
+    dim: 1024,
+    maxTokens: 512,
+    maxTextChars: 480,
+    dtype: 'fp32',
+    sizeLabel: '约 1.2 GB',
+    enabled: true
+  },
+  {
+    id: 'bge-m3',
+    displayName: 'BGE-M3 · 多语言',
+    description: '更强的多语言和长文本语义召回，资源占用更高。',
+    modelId: 'Xenova/bge-m3',
+    remoteHosts: [MODELSCOPE_HOST],
+    remotePathTemplate: MODELSCOPE_PATH_TEMPLATE,
+    revision: MODELSCOPE_REVISION,
+    dim: 1024,
+    maxTokens: 8192,
+    maxTextChars: 2400,
+    dtype: 'q8',
+    sizeLabel: '约 600 MB',
+    enabled: true
+  }
+]
+
+function safeProfileId(value: unknown): EmbeddingModelProfileId {
+  const id = String(value || '').trim() as EmbeddingModelProfileId
+  const profile = EMBEDDING_MODEL_PROFILES.find((item) => item.id === id && item.enabled)
+  return profile?.id || DEFAULT_EMBEDDING_MODEL_PROFILE
+}
+
+function safeEmbeddingDevice(value: unknown): EmbeddingDevice {
+  return String(value || '').trim() === 'dml' ? 'dml' : 'cpu'
+}
+
+function directorySize(dir: string): number {
+  if (!existsSync(dir)) return 0
+
+  let total = 0
+  for (const entry of readdirSync(dir, { withFileTypes: true })) {
+    const path = join(dir, entry.name)
+    if (entry.isDirectory()) {
+      total += directorySize(path)
+    } else if (entry.isFile()) {
+      total += statSync(path).size
+    }
+  }
+  return total
+}
+
+function hasModelFiles(dir: string): boolean {
+  if (!existsSync(dir)) return false
+
+  let hasOnnx = false
+  let hasTokenizer = false
+  const visit = (current: string) => {
+    for (const entry of readdirSync(current, { withFileTypes: true })) {
+      const path = join(current, entry.name)
+      if (entry.isDirectory()) {
+        visit(path)
+        continue
+      }
+
+      if (entry.name.endsWith('.onnx')) hasOnnx = true
+      if (entry.name === 'tokenizer.json' || entry.name === 'tokenizer_config.json') hasTokenizer = true
+    }
+  }
+
+  visit(dir)
+  return hasOnnx && hasTokenizer
+}
+
+function getElectronAppSafe(): any | null {
+  try {
+    const electronModule = require('electron')
+    const electronApp = electronModule && typeof electronModule === 'object' ? electronModule.app : null
+    return electronApp?.getPath ? electronApp : null
+  } catch {
+    return null
+  }
+}
+
+function getEffectiveCachePathFromConfig(): string {
+  const configService = new ConfigService()
+  try {
+    const configured = String(configService.get('cachePath' as any) || '').trim()
+    if (configured) return configured
+  } finally {
+    configService.close()
+  }
+
+  const electronApp = getElectronAppSafe()
+  if (electronApp?.getPath) {
+    const documentsPath = electronApp.getPath('documents')
+    if (process.env.VITE_DEV_SERVER_URL) {
+      return join(documentsPath, 'CipherTalkData')
+    }
+
+    const installDir = dirname(electronApp.getPath('exe'))
+    const isOnCDrive = /^[cC]:/i.test(installDir) || installDir.startsWith('\\\\')
+    return isOnCDrive ? join(documentsPath, 'CipherTalkData') : join(installDir, 'CipherTalkData')
+  }
+
+  return join(process.cwd(), 'CipherTalkData')
+}
+
+function getDirectMLDllPath(): string | null {
+  if (process.platform !== 'win32') return null
+
+  try {
+    const ortEntry = require.resolve('onnxruntime-node')
+    const arch = process.arch === 'arm64' ? 'arm64' : 'x64'
+    return join(dirname(ortEntry), '..', 'bin', 'napi-v6', 'win32', arch, 'DirectML.dll')
+  } catch {
+    return null
+  }
+}
+
+function limitEmbeddingText(text: string, maxChars: number): string {
+  const value = String(text || '')
+  const limit = Number.isFinite(maxChars) && maxChars > 0 ? Math.floor(maxChars) : 480
+  if (value.length <= limit) return value
+
+  const headLength = Math.max(1, Math.floor(limit * 0.75))
+  const tailLength = Math.max(1, limit - headLength)
+  return `${value.slice(0, headLength)}\n${value.slice(-tailLength)}`
+}
+
+function tensorToVectors(output: any, expectedCount: number): Float32Array[] {
+  const data = output?.data
+  const dims = Array.isArray(output?.dims) ? output.dims.map((item: unknown) => Number(item)) : []
+  if (!data || typeof data.length !== 'number' || dims.length === 0) {
+    throw new Error('Embedding 模型输出为空')
+  }
+
+  const dim = Number(dims[dims.length - 1] || 0)
+  const batch = dims.length >= 2 ? Number(dims[0] || expectedCount) : expectedCount
+  if (!Number.isInteger(dim) || dim <= 0) {
+    throw new Error('Embedding 模型输出维度无效')
+  }
+
+  const vectors: Float32Array[] = []
+  for (let index = 0; index < batch; index += 1) {
+    const start = index * dim
+    const end = start + dim
+    if (end > data.length) break
+    vectors.push(Float32Array.from(data.slice(start, end)))
+  }
+
+  if (vectors.length !== expectedCount) {
+    throw new Error(`Embedding 输出数量不匹配：${vectors.length}/${expectedCount}`)
+  }
+
+  return vectors
+}
+
+export function hashEmbeddingContent(value: string): string {
+  return createHash('sha256').update(value || '').digest('hex')
+}
+
+export function float32ArrayToBuffer(vector: Float32Array): Buffer {
+  return Buffer.from(vector.buffer.slice(vector.byteOffset, vector.byteOffset + vector.byteLength))
+}
+
+function meanPoolNormalize(output: any, attentionMask: any, expectedCount: number): Float32Array[] {
+  const hidden = output?.last_hidden_state || output?.token_embeddings || output?.logits
+  const hiddenData = hidden?.data
+  const hiddenDims = Array.isArray(hidden?.dims) ? hidden.dims.map((item: unknown) => Number(item)) : []
+  const maskData = attentionMask?.data
+  if (!hiddenData || hiddenDims.length !== 3 || !maskData) {
+    throw new Error('Embedding 模型输出为空')
+  }
+
+  const [batchSize, seqLength, dim] = hiddenDims
+  if (batchSize !== expectedCount || !Number.isInteger(seqLength) || !Number.isInteger(dim) || dim <= 0) {
+    throw new Error(`Embedding 输出维度无效：${hiddenDims.join('x')}`)
+  }
+
+  const vectors: Float32Array[] = []
+  for (let batch = 0; batch < batchSize; batch += 1) {
+    const vector = new Float32Array(dim)
+    let tokenCount = 0
+    for (let token = 0; token < seqLength; token += 1) {
+      const mask = Number(maskData[batch * seqLength + token] || 0)
+      if (mask <= 0) continue
+      tokenCount += mask
+      const offset = (batch * seqLength + token) * dim
+      for (let index = 0; index < dim; index += 1) {
+        vector[index] += Number(hiddenData[offset + index]) * mask
+      }
+    }
+
+    const divisor = tokenCount > 0 ? tokenCount : 1
+    let norm = 0
+    for (let index = 0; index < dim; index += 1) {
+      vector[index] /= divisor
+      norm += vector[index] * vector[index]
+    }
+    norm = Math.sqrt(norm) || 1
+    for (let index = 0; index < dim; index += 1) {
+      vector[index] /= norm
+    }
+    vectors.push(vector)
+  }
+
+  return vectors
+}
+
+export class LocalEmbeddingModelService {
+  private pipelines = new Map<string, Promise<{ tokenizer: any; model: any }>>()
+  private downloadTasks = new Map<string, Promise<EmbeddingModelStatus>>()
+  private dmlFailureReason: string | null = null
+
+  listProfiles(): EmbeddingModelProfile[] {
+    return EMBEDDING_MODEL_PROFILES.map((profile) => ({ ...profile }))
+  }
+
+  getProfile(profileId?: string): EmbeddingModelProfile {
+    const id = safeProfileId(profileId || this.getCurrentProfileId())
+    return EMBEDDING_MODEL_PROFILES.find((profile) => profile.id === id)!
+  }
+
+  getCurrentProfileId(): EmbeddingModelProfileId {
+    const configService = new ConfigService()
+    try {
+      return safeProfileId(configService.get('aiEmbeddingModelProfile' as any))
+    } finally {
+      configService.close()
+    }
+  }
+
+  setCurrentProfileId(profileId: string): EmbeddingModelProfileId {
+    const id = safeProfileId(profileId)
+    const configService = new ConfigService()
+    try {
+      configService.set('aiEmbeddingModelProfile' as any, id)
+      return id
+    } finally {
+      configService.close()
+    }
+  }
+
+  getCurrentDevice(): EmbeddingDevice {
+    const configService = new ConfigService()
+    try {
+      return safeEmbeddingDevice(configService.get('aiEmbeddingDevice' as any))
+    } finally {
+      configService.close()
+    }
+  }
+
+  setCurrentDevice(device: string): EmbeddingDevice {
+    const nextDevice = safeEmbeddingDevice(device)
+    const configService = new ConfigService()
+    try {
+      configService.set('aiEmbeddingDevice' as any, nextDevice)
+      this.dmlFailureReason = null
+      this.clearPipelines()
+      return nextDevice
+    } finally {
+      configService.close()
+    }
+  }
+
+  getDeviceStatus(): EmbeddingDeviceStatus {
+    const currentDevice = this.getCurrentDevice()
+    const directMLDll = getDirectMLDllPath()
+    const directMLAvailable = process.platform === 'win32' && !!directMLDll && existsSync(directMLDll)
+
+    if (currentDevice === 'dml' && this.dmlFailureReason) {
+      return {
+        currentDevice,
+        effectiveDevice: 'cpu',
+        gpuAvailable: directMLAvailable,
+        provider: 'CPU',
+        info: `DirectML 本次运行失败，已自动回退 CPU：${this.dmlFailureReason}`
+      }
+    }
+
+    if (currentDevice === 'dml' && directMLAvailable) {
+      return {
+        currentDevice,
+        effectiveDevice: 'dml',
+        gpuAvailable: true,
+        provider: 'DirectML',
+        info: 'DirectML 组件已就绪，将优先使用 GPU；推理失败时自动回退 CPU'
+      }
+    }
+
+    if (currentDevice === 'dml') {
+      return {
+        currentDevice,
+        effectiveDevice: 'cpu',
+        gpuAvailable: false,
+        provider: 'CPU',
+        info: process.platform === 'win32'
+          ? '缺少 DirectML 组件，将使用 CPU'
+          : '当前系统不支持 DirectML，将使用 CPU'
+      }
+    }
+
+    return {
+      currentDevice,
+      effectiveDevice: 'cpu',
+      gpuAvailable: directMLAvailable,
+      provider: 'CPU',
+      info: directMLAvailable ? '当前使用 CPU，可切换到 DirectML GPU 实验模式' : '当前使用 CPU'
+    }
+  }
+
+  getModelsRoot(): string {
+    return join(getEffectiveCachePathFromConfig(), 'models', 'embeddings')
+  }
+
+  getProfileDir(profileId?: string): string {
+    return join(this.getModelsRoot(), this.getProfile(profileId).id)
+  }
+
+  async getModelStatus(profileId?: string): Promise<EmbeddingModelStatus> {
+    const profile = this.getProfile(profileId)
+    const modelDir = this.getProfileDir(profile.id)
+    const exists = hasModelFiles(modelDir)
+    return {
+      profileId: profile.id,
+      displayName: profile.displayName,
+      modelId: profile.modelId,
+      dim: profile.dim,
+      dtype: profile.dtype,
+      sizeLabel: profile.sizeLabel,
+      enabled: profile.enabled,
+      exists,
+      modelDir,
+      sizeBytes: directorySize(modelDir)
+    }
+  }
+
+  async downloadModel(
+    profileId?: string,
+    onProgress?: (progress: EmbeddingDownloadProgress) => void
+  ): Promise<EmbeddingModelStatus> {
+    const profile = this.getProfile(profileId)
+    const existing = this.downloadTasks.get(profile.id)
+    if (existing) return existing
+
+    const task = (async () => {
+      mkdirSync(this.getProfileDir(profile.id), { recursive: true })
+      await this.downloadPipelineWithFallback(profile, onProgress)
+      return this.getModelStatus(profile.id)
+    })()
+
+    this.downloadTasks.set(profile.id, task)
+    try {
+      return await task
+    } finally {
+      this.downloadTasks.delete(profile.id)
+    }
+  }
+
+  async clearModel(profileId?: string): Promise<EmbeddingModelStatus> {
+    const profile = this.getProfile(profileId)
+    this.clearPipelines(profile.id)
+    rmSync(this.getProfileDir(profile.id), { recursive: true, force: true })
+    return this.getModelStatus(profile.id)
+  }
+
+  async ensureModelReady(profileId?: string): Promise<EmbeddingModelStatus> {
+    const status = await this.getModelStatus(profileId)
+    if (!status.exists) {
+      throw new Error(`本地语义模型未下载：${status.displayName}`)
+    }
+    return status
+  }
+
+  async embedTexts(texts: string[], profileId?: string): Promise<Float32Array[]> {
+    const profile = this.getProfile(profileId)
+    const cleaned = texts.map((text) => limitEmbeddingText(String(text || ''), profile.maxTextChars))
+    await this.ensureModelReady(profile.id)
+    const deviceStatus = this.getDeviceStatus()
+
+    if (deviceStatus.effectiveDevice === 'dml') {
+      try {
+        return await this.runEmbedding(profile, cleaned, 'dml')
+      } catch (error) {
+        console.warn('[Embedding] DirectML 推理失败，回退 CPU:', error)
+        this.dmlFailureReason = String(error instanceof Error ? error.message : error)
+        this.clearPipelines(profile.id, 'dml')
+      }
+    }
+
+    return this.runEmbedding(profile, cleaned, 'cpu')
+  }
+
+  async embedText(text: string, profileId?: string): Promise<Float32Array> {
+    const [vector] = await this.embedTexts([text], profileId)
+    return vector
+  }
+
+  private async runEmbedding(
+    profile: EmbeddingModelProfile,
+    texts: string[],
+    device: EmbeddingDevice
+  ): Promise<Float32Array[]> {
+    const runtime = await this.getPipeline(profile, true, device)
+    const modelInputs = runtime.tokenizer(texts, {
+      padding: true,
+      truncation: true,
+      max_length: profile.maxTokens
+    })
+    const output = await runtime.model(modelInputs)
+    return meanPoolNormalize(output, modelInputs.attention_mask, texts.length)
+  }
+
+  private async getPipeline(
+    profile: EmbeddingModelProfile,
+    localOnly: boolean,
+    device: EmbeddingDevice = 'cpu',
+    remoteHost?: string,
+    progressCallback?: (event: any) => void
+  ): Promise<{ tokenizer: any; model: any }> {
+    const key = `${profile.id}:${device}:${localOnly ? 'local' : remoteHost || 'remote'}`
+    const existing = this.pipelines.get(key)
+    if (existing) return existing
+
+    const promise = (async () => {
+      const transformers = await import('@huggingface/transformers')
+      transformers.env.allowLocalModels = true
+      transformers.env.allowRemoteModels = !localOnly
+      transformers.env.cacheDir = this.getProfileDir(profile.id)
+      if (remoteHost) {
+        transformers.env.remoteHost = remoteHost
+        transformers.env.remotePathTemplate = profile.remotePathTemplate
+      }
+
+      const commonOptions = {
+        cache_dir: this.getProfileDir(profile.id),
+        local_files_only: localOnly,
+        revision: profile.revision,
+        progress_callback: progressCallback
+      }
+      const tokenizer = await transformers.AutoTokenizer.from_pretrained(profile.modelId, commonOptions as any)
+      const model = await transformers.AutoModel.from_pretrained(profile.modelId, {
+        ...commonOptions,
+        device,
+        dtype: profile.dtype
+      } as any)
+      return { tokenizer, model }
+    })()
+
+    this.pipelines.set(key, promise)
+    try {
+      return await promise
+    } catch (error) {
+      this.pipelines.delete(key)
+      throw error
+    }
+  }
+
+  private clearPipelines(profileId?: string, device?: EmbeddingDevice): void {
+    for (const key of Array.from(this.pipelines.keys())) {
+      const matchesProfile = !profileId || key.startsWith(`${profileId}:`)
+      const matchesDevice = !device || key.includes(`:${device}:`)
+      if (matchesProfile && matchesDevice) {
+        this.pipelines.delete(key)
+      }
+    }
+  }
+
+  private async downloadPipelineWithFallback(
+    profile: EmbeddingModelProfile,
+    onProgress?: (progress: EmbeddingDownloadProgress) => void
+  ): Promise<void> {
+    const errors: string[] = []
+
+    for (const remoteHost of profile.remoteHosts) {
+      try {
+        onProgress?.({
+          profileId: profile.id,
+          displayName: profile.displayName,
+          remoteHost,
+          status: 'initiate'
+        })
+
+        await this.getPipeline(profile, false, 'cpu', remoteHost, (event) => {
+          const loaded = Number(event?.loaded || 0)
+          const total = Number(event?.total || 0)
+          onProgress?.({
+            profileId: profile.id,
+            displayName: profile.displayName,
+            remoteHost,
+            file: String(event?.file || event?.name || ''),
+            loaded: Number.isFinite(loaded) && loaded > 0 ? loaded : undefined,
+            total: Number.isFinite(total) && total > 0 ? total : undefined,
+            percent: total > 0 ? Math.min(100, Math.round((loaded / total) * 100)) : undefined,
+            status: String(event?.status || '')
+          })
+        })
+        return
+      } catch (error) {
+        errors.push(`${remoteHost}: ${String(error)}`)
+      }
+    }
+
+    throw new Error(`语义模型下载失败。已尝试 ModelScope/魔塔社区：${profile.remoteHosts.join('、')}。请检查网络/代理或稍后重试。${errors.length ? ` 原始错误：${errors.join(' | ')}` : ''}`)
+  }
+}
+
+export const localEmbeddingModelService = new LocalEmbeddingModelService()
diff --git a/package-lock.json b/package-lock.json
index ebc8fe2..72f1b00 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -12,6 +12,7 @@
       "dependencies": {
         "@emotion/react": "^11.14.0",
         "@emotion/styled": "^11.14.1",
+        "@huggingface/transformers": "^4.2.0",
         "@lobehub/fluent-emoji": "^4.1.0",
         "@lobehub/icons": "^5.3.0",
         "@lobehub/ui": "^5.6.5",
@@ -50,6 +51,7 @@
         "react-window": "^2.2.5",
         "sherpa-onnx-node": "^1.12.23",
         "silk-wasm": "^3.7.1",
+        "sqlite-vec": "^0.1.9",
         "wechat-emojis": "^1.0.2",
         "xlsx": "^0.18.5",
         "zod": "^4.1.12",
@@ -1024,7 +1026,6 @@
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-      "dev": true,
       "license": "MIT",
       "optional": true,
       "dependencies": {
@@ -1035,7 +1036,6 @@
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       "resolved": "https://registry.npmmirror.com/tslib/-/tslib-2.8.1.tgz",
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-      "dev": true,
       "license": "0BSD",
       "optional": true
     },
@@ -1755,6 +1755,34 @@
         "hono": "^4"
       }
     },
+    "node_modules/@huggingface/jinja": {
+      "version": "0.5.8",
+      "resolved": "https://registry.npmmirror.com/@huggingface/jinja/-/jinja-0.5.8.tgz",
+      "integrity": "sha512-ZdElB7DPS7QQS8ZnFc5RPPtkg+eN11z8AmIZWAyes6pSbwXqiFB/POVevvm01begdSX1ho9Gxln/F6qlQMsuaA==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=18"
+      }
+    },
+    "node_modules/@huggingface/tokenizers": {
+      "version": "0.1.3",
+      "resolved": "https://registry.npmmirror.com/@huggingface/tokenizers/-/tokenizers-0.1.3.tgz",
+      "integrity": "sha512-8rF/RRT10u+kn7YuUbUg0OF30K8rjTc78aHpxT+qJ1uWSqxT1MHi8+9ltwYfkFYJzT/oS+qw3JVfHtNMGAdqyA==",
+      "license": "Apache-2.0"
+    },
+    "node_modules/@huggingface/transformers": {
+      "version": "4.2.0",
+      "resolved": "https://registry.npmmirror.com/@huggingface/transformers/-/transformers-4.2.0.tgz",
+      "integrity": "sha512-8BRCoBMH0XsWaEIamuR0LrJGAfftgHAfb2Vrffy0VKlSAE/MnUJ5/h/zTfEP3fDIft+nk7TqB8xXEyABGitBjQ==",
+      "license": "Apache-2.0",
+      "dependencies": {
+        "@huggingface/jinja": "^0.5.6",
+        "@huggingface/tokenizers": "^0.1.3",
+        "onnxruntime-node": "1.24.3",
+        "onnxruntime-web": "1.26.0-dev.20260416-b7804b056c",
+        "sharp": "^0.34.5"
+      }
+    },
     "node_modules/@iconify/types": {
       "version": "2.0.0",
       "resolved": "https://registry.npmmirror.com/@iconify/types/-/types-2.0.0.tgz",
@@ -1776,7 +1804,6 @@
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       "resolved": "https://registry.npmmirror.com/@img/colour/-/colour-1.0.0.tgz",
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-      "dev": true,
       "license": "MIT",
       "engines": {
         "node": ">=18"
@@ -1789,7 +1816,6 @@
       "cpu": [
         "arm64"
       ],
-      "dev": true,
       "license": "Apache-2.0",
       "optional": true,
       "os": [
@@ -1812,7 +1838,6 @@
       "cpu": [
         "x64"
       ],
-      "dev": true,
       "license": "Apache-2.0",
       "optional": true,
       "os": [
@@ -1835,7 +1860,6 @@
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         "arm64"
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@@ -1852,7 +1876,6 @@
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         "x64"
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-      "dev": true,
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@@ -1869,7 +1892,6 @@
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@@ -1886,7 +1908,6 @@
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-      "dev": true,
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@@ -1903,7 +1924,6 @@
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       "license": "LGPL-3.0-or-later",
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@@ -1920,7 +1940,6 @@
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         "riscv64"
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-      "dev": true,
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@@ -1937,7 +1956,6 @@
       "cpu": [
         "s390x"
       ],
-      "dev": true,
       "license": "LGPL-3.0-or-later",
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@@ -1954,7 +1972,6 @@
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         "x64"
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@@ -1971,7 +1988,6 @@
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@@ -1988,7 +2004,6 @@
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@@ -2005,7 +2020,6 @@
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@@ -2028,7 +2042,6 @@
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@@ -2051,7 +2064,6 @@
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@@ -2074,7 +2086,6 @@
       "cpu": [
         "riscv64"
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-      "dev": true,
       "license": "Apache-2.0",
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       "os": [
@@ -2097,7 +2108,6 @@
       "cpu": [
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-      "dev": true,
       "license": "Apache-2.0",
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       "os": [
@@ -2120,7 +2130,6 @@
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-      "dev": true,
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@@ -2143,7 +2152,6 @@
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-      "dev": true,
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@@ -2166,7 +2174,6 @@
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-      "dev": true,
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       "os": [
@@ -2189,7 +2196,6 @@
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         "wasm32"
       ],
-      "dev": true,
       "license": "Apache-2.0 AND LGPL-3.0-or-later AND MIT",
       "optional": true,
       "dependencies": {
@@ -2209,7 +2215,6 @@
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-      "dev": true,
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@@ -2229,7 +2234,6 @@
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       ],
-      "dev": true,
       "license": "Apache-2.0 AND LGPL-3.0-or-later",
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@@ -2249,7 +2253,6 @@
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-      "dev": true,
       "license": "Apache-2.0 AND LGPL-3.0-or-later",
       "optional": true,
       "os": [
@@ -3431,6 +3434,70 @@
         "object-assign": "^4.1.1"
       }
     },
+    "node_modules/@protobufjs/aspromise": {
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+      "resolved": "https://registry.npmmirror.com/@protobufjs/aspromise/-/aspromise-1.1.2.tgz",
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+      "license": "BSD-3-Clause"
+    },
+    "node_modules/@protobufjs/base64": {
+      "version": "1.1.2",
+      "resolved": "https://registry.npmmirror.com/@protobufjs/base64/-/base64-1.1.2.tgz",
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+      "license": "BSD-3-Clause"
+    },
+    "node_modules/@protobufjs/codegen": {
+      "version": "2.0.4",
+      "resolved": "https://registry.npmmirror.com/@protobufjs/codegen/-/codegen-2.0.4.tgz",
+      "integrity": "sha512-YyFaikqM5sH0ziFZCN3xDC7zeGaB/d0IUb9CATugHWbd1FRFwWwt4ld4OYMPWu5a3Xe01mGAULCdqhMlPl29Jg==",
+      "license": "BSD-3-Clause"
+    },
+    "node_modules/@protobufjs/eventemitter": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmmirror.com/@protobufjs/eventemitter/-/eventemitter-1.1.0.tgz",
+      "integrity": "sha512-j9ednRT81vYJ9OfVuXG6ERSTdEL1xVsNgqpkxMsbIabzSo3goCjDIveeGv5d03om39ML71RdmrGNjG5SReBP/Q==",
+      "license": "BSD-3-Clause"
+    },
+    "node_modules/@protobufjs/fetch": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmmirror.com/@protobufjs/fetch/-/fetch-1.1.0.tgz",
+      "integrity": "sha512-lljVXpqXebpsijW71PZaCYeIcE5on1w5DlQy5WH6GLbFryLUrBD4932W/E2BSpfRJWseIL4v/KPgBFxDOIdKpQ==",
+      "license": "BSD-3-Clause",
+      "dependencies": {
+        "@protobufjs/aspromise": "^1.1.1",
+        "@protobufjs/inquire": "^1.1.0"
+      }
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+    "node_modules/@protobufjs/float": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmmirror.com/@protobufjs/float/-/float-1.0.2.tgz",
+      "integrity": "sha512-Ddb+kVXlXst9d+R9PfTIxh1EdNkgoRe5tOX6t01f1lYWOvJnSPDBlG241QLzcyPdoNTsblLUdujGSE4RzrTZGQ==",
+      "license": "BSD-3-Clause"
+    },
+    "node_modules/@protobufjs/inquire": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmmirror.com/@protobufjs/inquire/-/inquire-1.1.0.tgz",
+      "integrity": "sha512-kdSefcPdruJiFMVSbn801t4vFK7KB/5gd2fYvrxhuJYg8ILrmn9SKSX2tZdV6V+ksulWqS7aXjBcRXl3wHoD9Q==",
+      "license": "BSD-3-Clause"
+    },
+    "node_modules/@protobufjs/path": {
+      "version": "1.1.2",
+      "resolved": "https://registry.npmmirror.com/@protobufjs/path/-/path-1.1.2.tgz",
+      "integrity": "sha512-6JOcJ5Tm08dOHAbdR3GrvP+yUUfkjG5ePsHYczMFLq3ZmMkAD98cDgcT2iA1lJ9NVwFd4tH/iSSoe44YWkltEA==",
+      "license": "BSD-3-Clause"
+    },
+    "node_modules/@protobufjs/pool": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmmirror.com/@protobufjs/pool/-/pool-1.1.0.tgz",
+      "integrity": "sha512-0kELaGSIDBKvcgS4zkjz1PeddatrjYcmMWOlAuAPwAeccUrPHdUqo/J6LiymHHEiJT5NrF1UVwxY14f+fy4WQw==",
+      "license": "BSD-3-Clause"
+    },
+    "node_modules/@protobufjs/utf8": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmmirror.com/@protobufjs/utf8/-/utf8-1.1.0.tgz",
+      "integrity": "sha512-Vvn3zZrhQZkkBE8LSuW3em98c0FwgO4nxzv6OdSxPKJIEKY2bGbHn+mhGIPerzI4twdxaP8/0+06HBpwf345Lw==",
+      "license": "BSD-3-Clause"
+    },
     "node_modules/@radix-ui/primitive": {
       "version": "1.1.3",
       "resolved": "https://registry.npmmirror.com/@radix-ui/primitive/-/primitive-1.1.3.tgz",
@@ -10417,6 +10484,12 @@
       "integrity": "sha512-NKfW6bec6GfKc0SGx1e07QZY9PE99u0Bft/0rzSD5k3sO/vwkVUpDUKVm5Gpp5Ue3YfShPFTX2070tDs5kB9Ng==",
       "license": "MIT"
     },
+    "node_modules/flatbuffers": {
+      "version": "25.9.23",
+      "resolved": "https://registry.npmmirror.com/flatbuffers/-/flatbuffers-25.9.23.tgz",
+      "integrity": "sha512-MI1qs7Lo4Syw0EOzUl0xjs2lsoeqFku44KpngfIduHBYvzm8h2+7K8YMQh1JtVVVrUvhLpNwqVi4DERegUJhPQ==",
+      "license": "Apache-2.0"
+    },
     "node_modules/for-in": {
       "version": "1.0.2",
       "resolved": "https://registry.npmmirror.com/for-in/-/for-in-1.0.2.tgz",
@@ -10882,6 +10955,12 @@
       "integrity": "sha512-RbJ5/jmFcNNCcDV5o9eTnBLJ/HszWV0P73bc+Ff4nS/rJj+YaS6IGyiOL0VoBYX+l1Wrl3k63h/KrH+nhJ0XvQ==",
       "license": "ISC"
     },
+    "node_modules/guid-typescript": {
+      "version": "1.0.9",
+      "resolved": "https://registry.npmmirror.com/guid-typescript/-/guid-typescript-1.0.9.tgz",
+      "integrity": "sha512-Y8T4vYhEfwJOTbouREvG+3XDsjr8E3kIr7uf+JZ0BYloFsttiHU0WfvANVsR7TxNUJa/WpCnw/Ino/p+DeBhBQ==",
+      "license": "ISC"
+    },
     "node_modules/hachure-fill": {
       "version": "0.5.2",
       "resolved": "https://registry.npmmirror.com/hachure-fill/-/hachure-fill-0.5.2.tgz",
@@ -12208,6 +12287,12 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
+    "node_modules/long": {
+      "version": "5.3.2",
+      "resolved": "https://registry.npmmirror.com/long/-/long-5.3.2.tgz",
+      "integrity": "sha512-mNAgZ1GmyNhD7AuqnTG3/VQ26o760+ZYBPKjPvugO8+nLbYfX6TVpJPseBvopbdY+qpZ/lKUnmEc1LeZYS3QAA==",
+      "license": "Apache-2.0"
+    },
     "node_modules/longest-streak": {
       "version": "3.1.0",
       "resolved": "https://registry.npmmirror.com/longest-streak/-/longest-streak-3.1.0.tgz",
@@ -14207,15 +14292,15 @@
       }
     },
     "node_modules/onnxruntime-common": {
-      "version": "1.24.1",
-      "resolved": "https://registry.npmmirror.com/onnxruntime-common/-/onnxruntime-common-1.24.1.tgz",
-      "integrity": "sha512-UnV15u4p4XxoIV+jFP4hXPsW93s3QrwLSpi20HUDYHoTfI4z4sjzex3L4XDOxGGZJ/M/catrwAG2go958UQq0w==",
+      "version": "1.24.3",
+      "resolved": "https://registry.npmmirror.com/onnxruntime-common/-/onnxruntime-common-1.24.3.tgz",
+      "integrity": "sha512-GeuPZO6U/LBJXvwdaqHbuUmoXiEdeCjWi/EG7Y1HNnDwJYuk6WUbNXpF6luSUY8yASul3cmUlLGrCCL1ZgVXqA==",
       "license": "MIT"
     },
     "node_modules/onnxruntime-node": {
-      "version": "1.24.1",
-      "resolved": "https://registry.npmmirror.com/onnxruntime-node/-/onnxruntime-node-1.24.1.tgz",
-      "integrity": "sha512-Ex/oUXKdhDoxvlNxBT3oYtW0MH88yYpPlXQeVQUXpcJQmN24usd/8RCoPLN5kCHwDsiZ+nqsnjciyFRl423dQw==",
+      "version": "1.24.3",
+      "resolved": "https://registry.npmmirror.com/onnxruntime-node/-/onnxruntime-node-1.24.3.tgz",
+      "integrity": "sha512-JH7+czbc8ALA819vlTgcV+Q214/+VjGeBHDjX81+ZCD0PCVCIFGFNtT0V4sXG/1JXypKPgScQcB3ij/hk3YnTg==",
       "hasInstallScript": true,
       "license": "MIT",
       "os": [
@@ -14226,9 +14311,29 @@
       "dependencies": {
         "adm-zip": "^0.5.16",
         "global-agent": "^3.0.0",
-        "onnxruntime-common": "1.24.1"
+        "onnxruntime-common": "1.24.3"
       }
     },
+    "node_modules/onnxruntime-web": {
+      "version": "1.26.0-dev.20260416-b7804b056c",
+      "resolved": "https://registry.npmmirror.com/onnxruntime-web/-/onnxruntime-web-1.26.0-dev.20260416-b7804b056c.tgz",
+      "integrity": "sha512-MD6Ss4GSpQBo6zqoJzyT9LRbKYs7x/JVN23FT24EcEvlqF4VuzPOeH6X38orZPKHQDbprn7K+SBpu0/mj2CQiw==",
+      "license": "MIT",
+      "dependencies": {
+        "flatbuffers": "^25.1.24",
+        "guid-typescript": "^1.0.9",
+        "long": "^5.2.3",
+        "onnxruntime-common": "1.24.0-dev.20251116-b39e144322",
+        "platform": "^1.3.6",
+        "protobufjs": "^7.2.4"
+      }
+    },
+    "node_modules/onnxruntime-web/node_modules/onnxruntime-common": {
+      "version": "1.24.0-dev.20251116-b39e144322",
+      "resolved": "https://registry.npmmirror.com/onnxruntime-common/-/onnxruntime-common-1.24.0-dev.20251116-b39e144322.tgz",
+      "integrity": "sha512-BOoomdHYmNRL5r4iQ4bMvsl2t0/hzVQ3OM3PHD0gxeXu1PmggqBv3puZicEUVOA3AtHHYmqZtjMj9FOfGrATTw==",
+      "license": "MIT"
+    },
     "node_modules/openai": {
       "version": "4.104.0",
       "resolved": "https://registry.npmmirror.com/openai/-/openai-4.104.0.tgz",
@@ -14578,6 +14683,12 @@
         "pathe": "^2.0.1"
       }
     },
+    "node_modules/platform": {
+      "version": "1.3.6",
+      "resolved": "https://registry.npmmirror.com/platform/-/platform-1.3.6.tgz",
+      "integrity": "sha512-fnWVljUchTro6RiCFvCXBbNhJc2NijN7oIQxbwsyL0buWJPG85v81ehlHI9fXrJsMNgTofEoWIQeClKpgxFLrg==",
+      "license": "MIT"
+    },
     "node_modules/plist": {
       "version": "3.1.0",
       "resolved": "https://registry.npmmirror.com/plist/-/plist-3.1.0.tgz",
@@ -14771,6 +14882,30 @@
         "url": "https://github.com/sponsors/wooorm"
       }
     },
+    "node_modules/protobufjs": {
+      "version": "7.5.5",
+      "resolved": "https://registry.npmmirror.com/protobufjs/-/protobufjs-7.5.5.tgz",
+      "integrity": "sha512-3wY1AxV+VBNW8Yypfd1yQY9pXnqTAN+KwQxL8iYm3/BjKYMNg4i0owhEe26PWDOMaIrzeeF98Lqd5NGz4omiIg==",
+      "hasInstallScript": true,
+      "license": "BSD-3-Clause",
+      "dependencies": {
+        "@protobufjs/aspromise": "^1.1.2",
+        "@protobufjs/base64": "^1.1.2",
+        "@protobufjs/codegen": "^2.0.4",
+        "@protobufjs/eventemitter": "^1.1.0",
+        "@protobufjs/fetch": "^1.1.0",
+        "@protobufjs/float": "^1.0.2",
+        "@protobufjs/inquire": "^1.1.0",
+        "@protobufjs/path": "^1.1.2",
+        "@protobufjs/pool": "^1.1.0",
+        "@protobufjs/utf8": "^1.1.0",
+        "@types/node": ">=13.7.0",
+        "long": "^5.0.0"
+      },
+      "engines": {
+        "node": ">=12.0.0"
+      }
+    },
     "node_modules/proxy-addr": {
       "version": "2.0.7",
       "resolved": "https://registry.npmmirror.com/proxy-addr/-/proxy-addr-2.0.7.tgz",
@@ -16292,7 +16427,6 @@
       "version": "0.34.5",
       "resolved": "https://registry.npmmirror.com/sharp/-/sharp-0.34.5.tgz",
       "integrity": "sha512-Ou9I5Ft9WNcCbXrU9cMgPBcCK8LiwLqcbywW3t4oDV37n1pzpuNLsYiAV8eODnjbtQlSDwZ2cUEeQz4E54Hltg==",
-      "dev": true,
       "hasInstallScript": true,
       "license": "Apache-2.0",
       "dependencies": {
@@ -16808,6 +16942,84 @@
       "integrity": "sha512-Oo+0REFV59/rz3gfJNKQiBlwfHaSESl1pcGyABQsnnIfWOFt6JNj5gCog2U6MLZ//IGYD+nA8nI+mTShREReaA==",
       "license": "BSD-3-Clause"
     },
+    "node_modules/sqlite-vec": {
+      "version": "0.1.9",
+      "resolved": "https://registry.npmmirror.com/sqlite-vec/-/sqlite-vec-0.1.9.tgz",
+      "integrity": "sha512-L7XJWRIBNvR9O5+vh1FQ+IGkh/3D2AzVksW5gdtk28m78Hy8skFD0pqReKH1Yp0/BUKRGcffgKvyO/EON5JXpA==",
+      "license": "MIT OR Apache",
+      "optionalDependencies": {
+        "sqlite-vec-darwin-arm64": "0.1.9",
+        "sqlite-vec-darwin-x64": "0.1.9",
+        "sqlite-vec-linux-arm64": "0.1.9",
+        "sqlite-vec-linux-x64": "0.1.9",
+        "sqlite-vec-windows-x64": "0.1.9"
+      }
+    },
+    "node_modules/sqlite-vec-darwin-arm64": {
+      "version": "0.1.9",
+      "resolved": "https://registry.npmmirror.com/sqlite-vec-darwin-arm64/-/sqlite-vec-darwin-arm64-0.1.9.tgz",
+      "integrity": "sha512-jSsZpE42OfBkGL/ItyJTVCUwl6o6Ka3U5rc4j+UBDIQzC1ulSSKMEhQLthsOnF/MdAf1MuAkYhkdKmmcjaIZQg==",
+      "cpu": [
+        "arm64"
+      ],
+      "license": "MIT OR Apache",
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/sqlite-vec-darwin-x64": {
+      "version": "0.1.9",
+      "resolved": "https://registry.npmmirror.com/sqlite-vec-darwin-x64/-/sqlite-vec-darwin-x64-0.1.9.tgz",
+      "integrity": "sha512-KDlVyqQT7pnOhU1ymB9gs7dMbSoVmKHitT+k1/xkjarcX8bBqPxWrGlK/R+C5WmWkfvWwyq5FfXfiBYCBs6PlA==",
+      "cpu": [
+        "x64"
+      ],
+      "license": "MIT OR Apache",
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/sqlite-vec-linux-arm64": {
+      "version": "0.1.9",
+      "resolved": "https://registry.npmmirror.com/sqlite-vec-linux-arm64/-/sqlite-vec-linux-arm64-0.1.9.tgz",
+      "integrity": "sha512-5wXVJ9c9kR4CHm/wVqXb/R+XUHTdpZ4nWbPHlS+gc9qQFVHs92Km4bPnCKX4rtcPMzvNis+SIzMJR1SCEwpuUw==",
+      "cpu": [
+        "arm64"
+      ],
+      "license": "MIT OR Apache",
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/sqlite-vec-linux-x64": {
+      "version": "0.1.9",
+      "resolved": "https://registry.npmmirror.com/sqlite-vec-linux-x64/-/sqlite-vec-linux-x64-0.1.9.tgz",
+      "integrity": "sha512-w3tCH8xK2finW8fQJ/m8uqKodXUZ9KAuAar2UIhz4BHILfpE0WM/MTGCRfa7RjYbrYim5Luk3guvMOGI7T7JQA==",
+      "cpu": [
+        "x64"
+      ],
+      "license": "MIT OR Apache",
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/sqlite-vec-windows-x64": {
+      "version": "0.1.9",
+      "resolved": "https://registry.npmmirror.com/sqlite-vec-windows-x64/-/sqlite-vec-windows-x64-0.1.9.tgz",
+      "integrity": "sha512-y3gEIyy/17bq2QFPQOWLE68TYWcRZkBQVA2XLrTPHNTOp55xJi/BBBmOm40tVMDMjtP+Elpk6UBUXdaq+46b0Q==",
+      "cpu": [
+        "x64"
+      ],
+      "license": "MIT OR Apache",
+      "optional": true,
+      "os": [
+        "win32"
+      ]
+    },
     "node_modules/ssf": {
       "version": "0.11.2",
       "resolved": "https://registry.npmmirror.com/ssf/-/ssf-0.11.2.tgz",
diff --git a/package.json b/package.json
index 56e191d..421e03c 100644
--- a/package.json
+++ b/package.json
@@ -38,6 +38,7 @@
   "dependencies": {
     "@emotion/react": "^11.14.0",
     "@emotion/styled": "^11.14.1",
+    "@huggingface/transformers": "^4.2.0",
     "@lobehub/fluent-emoji": "^4.1.0",
     "@lobehub/icons": "^5.3.0",
     "@lobehub/ui": "^5.6.5",
@@ -76,6 +77,7 @@
     "react-window": "^2.2.5",
     "sherpa-onnx-node": "^1.12.23",
     "silk-wasm": "^3.7.1",
+    "sqlite-vec": "^0.1.9",
     "wechat-emojis": "^1.0.2",
     "xlsx": "^0.18.5",
     "zod": "^4.1.12",
@@ -211,12 +213,14 @@
       "!node_modules/**/*.lib",
       "!node_modules/**/build/!(Release)/**/*",
       "!node_modules/**/deps/**/*",
+      "node_modules/sqlite-vec*/**/*",
       "node_modules/koffi/build/**/*"
     ],
     "asarUnpack": [
       "node_modules/ffmpeg-static/**/*",
       "node_modules/silk-wasm/**/*",
       "node_modules/sherpa-onnx-node/**/*",
+      "node_modules/sqlite-vec*/**/*",
       "node_modules/koffi/**/*",
       "dist-electron/workers/**/*",
       "resources/wedecrypt/*.node",
diff --git a/scripts/electron-builder.config.cjs b/scripts/electron-builder.config.cjs
index 313f6ee..9862f73 100644
--- a/scripts/electron-builder.config.cjs
+++ b/scripts/electron-builder.config.cjs
@@ -91,6 +91,7 @@ function getFiles(buildTarget) {
       [
         ...commonFiles,
         '!node_modules/onnxruntime-node/bin/**/win32/arm64/**/*',
+        'node_modules/sqlite-vec*/**/*',
         'node_modules/koffi/build/koffi/win32_x64/**/*'
       ]
     )
@@ -108,6 +109,9 @@ function getFiles(buildTarget) {
         '!node_modules/onnxruntime-node/bin/**/linux/**/*',
         '!node_modules/onnxruntime-node/bin/**/win32/**/*',
         'node_modules/onnxruntime-node/bin/**/darwin/**/*',
+        '!node_modules/sqlite-vec-windows-*/**/*',
+        '!node_modules/sqlite-vec-linux-*/**/*',
+        'node_modules/sqlite-vec-darwin-*/**/*',
         '!node_modules/sherpa-onnx-win-*/**/*',
         '!node_modules/sherpa-onnx-linux-*/**/*',
         'node_modules/sherpa-onnx-darwin-*/**/*',
@@ -128,14 +132,14 @@ function getAsarUnpack(buildTarget) {
   if (buildTarget === 'win') {
     return appendUnique(
       withoutItems(baseAsarUnpack, ['node_modules/koffi/**/*']),
-      ['node_modules/koffi/build/koffi/win32_x64/**/*']
+      ['node_modules/sqlite-vec*/**/*', 'node_modules/koffi/build/koffi/win32_x64/**/*']
     )
   }
 
   if (buildTarget === 'mac') {
     return appendUnique(
       withoutItems(baseAsarUnpack, ['node_modules/koffi/**/*']),
-      ['node_modules/koffi/build/koffi/darwin_*/**/*']
+      ['node_modules/sqlite-vec*/**/*', 'node_modules/koffi/build/koffi/darwin_*/**/*']
     )
   }
 
diff --git a/src/components/ai/AISummarySettings.scss b/src/components/ai/AISummarySettings.scss
index 8bcb67c..016a9de 100644
--- a/src/components/ai/AISummarySettings.scss
+++ b/src/components/ai/AISummarySettings.scss
@@ -484,6 +484,75 @@
     }
   }
 
+  .semantic-vector-subtitle {
+    margin-top: 1rem;
+    margin-bottom: 0.5rem;
+    font-size: 0.95rem;
+    font-weight: 500;
+  }
+
+  .semantic-model-grid {
+    margin-bottom: 16px;
+  }
+
+  .semantic-device-grid {
+    grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
+    margin-bottom: 12px;
+  }
+
+  .semantic-device-status {
+    padding: 14px 16px;
+    margin-bottom: 16px;
+    background: var(--bg-tertiary);
+    border-radius: 12px;
+
+    .status-indicator {
+      display: flex;
+      align-items: center;
+      gap: 8px;
+      font-size: 14px;
+      font-weight: 500;
+
+      &.ready {
+        color: #22c55e;
+      }
+
+      &.missing {
+        color: #f59e0b;
+      }
+    }
+
+    p {
+      margin: 8px 0 0;
+      color: var(--text-secondary);
+      font-size: 13px;
+      line-height: 1.5;
+    }
+  }
+
+  .semantic-vector-model-status {
+    .model-size span {
+      color: var(--text-tertiary);
+    }
+
+    .model-path {
+      margin: 0;
+      color: var(--text-tertiary);
+      font-size: 12px;
+      line-height: 1.5;
+      word-break: break-all;
+    }
+  }
+
+  .semantic-download-progress {
+    margin-top: 0;
+  }
+
+  .semantic-vector-actions {
+    margin-top: 1rem;
+    margin-bottom: 24px;
+  }
+
   // 3. 详情卡片
   .detail-options {
     display: grid;
@@ -1443,4 +1512,4 @@
       }
     }
   }
-}
\ No newline at end of file
+}
diff --git a/src/components/ai/AISummarySettings.tsx b/src/components/ai/AISummarySettings.tsx
index d50fc6f..7a0e4cf 100644
--- a/src/components/ai/AISummarySettings.tsx
+++ b/src/components/ai/AISummarySettings.tsx
@@ -1,6 +1,6 @@
 import { useState, useEffect, useRef } from 'react'
-import { Eye, EyeOff, Sparkles, Check, ChevronDown, ChevronUp, Zap, Star, FileText, HelpCircle, X, Plus, Settings2 } from 'lucide-react'
-import { getAIProviders, type AIProviderInfo } from '../../types/ai'
+import { Eye, EyeOff, Sparkles, Check, ChevronDown, ChevronUp, Zap, Star, FileText, HelpCircle, X, Plus, Settings2, Download, Trash2, Database, CheckCircle, AlertCircle, RefreshCw, Layers, Cpu } from 'lucide-react'
+import { getAIProviders, type AIProviderInfo, type EmbeddingDevice, type EmbeddingDeviceStatus, type EmbeddingModelDownloadProgress, type EmbeddingModelProfile, type EmbeddingModelStatus } from '../../types/ai'
 import { marked } from 'marked'
 import DOMPurify from 'dompurify'
 import AIProviderLogo from './AIProviderLogo'
@@ -130,6 +130,19 @@ function normalizeProviderModel(providerId: string, modelName: string) {
   return DEEPSEEK_LEGACY_MODEL_MAP[modelName] || modelName
 }
 
+function formatBytes(bytes?: number): string {
+  const value = Number(bytes || 0)
+  if (value <= 0) return '0 B'
+  const units = ['B', 'KB', 'MB', 'GB']
+  let size = value
+  let unitIndex = 0
+  while (size >= 1024 && unitIndex < units.length - 1) {
+    size /= 1024
+    unitIndex += 1
+  }
+  return `${size.toFixed(unitIndex === 0 ? 0 : 1)} ${units[unitIndex]}`
+}
+
 function AISummarySettings({
   provider,
   setProvider,
@@ -173,6 +186,14 @@ function AISummarySettings({
   const [newPresetBaseURL, setNewPresetBaseURL] = useState('')
   const [currentPresetName, setCurrentPresetName] = useState('')
   const [editingPresetId, setEditingPresetId] = useState<string | null>(null)
+  const [embeddingProfiles, setEmbeddingProfiles] = useState<EmbeddingModelProfile[]>([])
+  const [embeddingProfileId, setEmbeddingProfileId] = useState('bge-large-zh-v1.5-int8')
+  const [embeddingDevice, setEmbeddingDevice] = useState<EmbeddingDevice>('cpu')
+  const [embeddingDeviceStatus, setEmbeddingDeviceStatus] = useState<EmbeddingDeviceStatus | null>(null)
+  const [embeddingStatus, setEmbeddingStatus] = useState<EmbeddingModelStatus | null>(null)
+  const [embeddingProgress, setEmbeddingProgress] = useState<EmbeddingModelDownloadProgress | null>(null)
+  const [isDownloadingEmbedding, setIsDownloadingEmbedding] = useState(false)
+  const [isClearingEmbedding, setIsClearingEmbedding] = useState(false)
 
   useEffect(() => {
     // 加载提供商列表和统计数据
@@ -180,8 +201,24 @@ function AISummarySettings({
     loadUsageStats()
     loadAllProviderConfigs()
     loadPresets()
+    loadEmbeddingModels()
+    loadEmbeddingDeviceStatus()
   }, [])
 
+  useEffect(() => {
+    if (!embeddingProfileId) return
+    loadEmbeddingStatus(embeddingProfileId)
+  }, [embeddingProfileId])
+
+  useEffect(() => {
+    const cleanup = window.electronAPI.ai.onEmbeddingModelDownloadProgress((progress) => {
+      if (progress.profileId === embeddingProfileId) {
+        setEmbeddingProgress(progress)
+      }
+    })
+    return cleanup
+  }, [embeddingProfileId])
+
   useEffect(() => {
     const normalizedModel = normalizeProviderModel(provider, model)
     if (normalizedModel !== model) {
@@ -258,6 +295,123 @@ function AISummarySettings({
     }
   }
 
+  const loadEmbeddingModels = async () => {
+    try {
+      const result = await window.electronAPI.ai.getEmbeddingModelProfiles()
+      if (result.success && result.result) {
+        setEmbeddingProfiles(result.result)
+        setEmbeddingProfileId(result.currentProfileId || 'bge-large-zh-v1.5-int8')
+      }
+    } catch (e) {
+      console.error('加载语义模型失败:', e)
+    }
+  }
+
+  const loadEmbeddingStatus = async (profileId = embeddingProfileId) => {
+    try {
+      const result = await window.electronAPI.ai.getEmbeddingModelStatus(profileId)
+      if (result.success && result.result) {
+        setEmbeddingStatus(result.result)
+      }
+    } catch (e) {
+      console.error('加载语义模型状态失败:', e)
+    }
+  }
+
+  const loadEmbeddingDeviceStatus = async () => {
+    try {
+      const result = await window.electronAPI.ai.getEmbeddingDeviceStatus()
+      if (result.success && result.result) {
+        setEmbeddingDevice(result.result.currentDevice)
+        setEmbeddingDeviceStatus(result.result)
+      }
+    } catch (e) {
+      console.error('加载语义向量计算模式失败:', e)
+    }
+  }
+
+  const handleEmbeddingDeviceChange = async (device: EmbeddingDevice) => {
+    setEmbeddingDevice(device)
+    const result = await window.electronAPI.ai.setEmbeddingDevice(device)
+    if (!result.success) {
+      showMessage(result.error || '语义向量计算模式设置失败', false)
+      await loadEmbeddingDeviceStatus()
+      return
+    }
+    if (result.status) {
+      setEmbeddingDeviceStatus(result.status)
+      setEmbeddingDevice(result.status.currentDevice)
+    } else {
+      await loadEmbeddingDeviceStatus()
+    }
+    showMessage(device === 'dml' ? '已启用 DirectML GPU 实验模式' : '已切换到 CPU 计算模式', true)
+  }
+
+  const handleEmbeddingProfileChange = async (profileId: string | number) => {
+    const nextProfileId = String(profileId)
+    const profile = embeddingProfiles.find((item) => item.id === nextProfileId)
+    if (profile && !profile.enabled) return
+
+    setEmbeddingProfileId(nextProfileId)
+    setEmbeddingProgress(null)
+    const result = await window.electronAPI.ai.setEmbeddingModelProfile(nextProfileId)
+    if (!result.success) {
+      showMessage(result.error || '语义模型设置失败', false)
+      return
+    }
+    await loadEmbeddingStatus(nextProfileId)
+    showMessage('语义模型设置已保存', true)
+  }
+
+  const handleDownloadEmbeddingModel = async () => {
+    if (!embeddingProfileId || isDownloadingEmbedding) return
+    setIsDownloadingEmbedding(true)
+    setEmbeddingProgress(null)
+    try {
+      const result = await window.electronAPI.ai.downloadEmbeddingModel(embeddingProfileId)
+      if (!result.success || !result.result) {
+        throw new Error(result.error || '语义模型下载失败')
+      }
+      setEmbeddingStatus(result.result)
+      setEmbeddingProgress(null)
+      showMessage('语义模型下载完成', true)
+    } catch (e) {
+      showMessage(String(e), false)
+    } finally {
+      setIsDownloadingEmbedding(false)
+    }
+  }
+
+  const handleClearEmbeddingModel = async () => {
+    if (!embeddingProfileId || isClearingEmbedding) return
+    setIsClearingEmbedding(true)
+    try {
+      const result = await window.electronAPI.ai.clearEmbeddingModel(embeddingProfileId)
+      if (!result.success || !result.result) {
+        throw new Error(result.error || '语义模型清理失败')
+      }
+      setEmbeddingStatus(result.result)
+      setEmbeddingProgress(null)
+      showMessage('语义模型已清理', true)
+    } catch (e) {
+      showMessage(String(e), false)
+    } finally {
+      setIsClearingEmbedding(false)
+    }
+  }
+
+  const handleClearSemanticIndex = async () => {
+    try {
+      const result = await window.electronAPI.ai.clearSemanticVectorIndex(embeddingProfileId)
+      if (!result.success || !result.result) {
+        throw new Error(result.error || '语义索引清理失败')
+      }
+      showMessage(`已清理 ${result.result.deletedCount} 条语义索引`, true)
+    } catch (e) {
+      showMessage(String(e), false)
+    }
+  }
+
   const handleStartNewPreset = () => {
     setEditingPresetId(null)
     setNewPresetStep('provider')
@@ -489,6 +643,9 @@ function AISummarySettings({
 
   const currentProvider = providers.find(p => p.id === provider) || providers[0]
   const modelOptions = currentProvider?.models.map(m => ({ value: m, label: m })) || []
+  const embeddingProgressPercent = embeddingProgress?.percent
+    ?? (embeddingProgress?.total ? Math.round(((embeddingProgress.loaded || 0) / embeddingProgress.total) * 100) : 0)
+  const embeddingProfile = embeddingProfiles.find(item => item.id === embeddingProfileId)
   const timeRangeOptions = [
     { value: 1, label: '最近 1 天' },
     { value: 3, label: '最近 3 天' },
@@ -704,6 +861,173 @@ function AISummarySettings({
         </div>
       </div>
 
+      <h3 className="section-title">本地语义检索</h3>
+      <p className="section-desc">
+        使用 ModelScope/魔塔社区的 BGE 模型在本地生成真实语义向量，用于问 AI 检索聊天记录。模型下载到缓存目录，仅需下载一次。
+      </p>
+
+      <h4 className="subsection-title semantic-vector-subtitle">语义模型版本</h4>
+      <div className="model-type-grid semantic-model-grid">
+        {embeddingProfiles.map(profile => (
+          <label
+            key={profile.id}
+            className={`model-card ${embeddingProfileId === profile.id ? 'active' : ''} ${isDownloadingEmbedding || !profile.enabled ? 'disabled' : ''}`}
+          >
+            <input
+              type="radio"
+              name="aiEmbeddingModelProfile"
+              value={profile.id}
+              checked={embeddingProfileId === profile.id}
+              onChange={() => handleEmbeddingProfileChange(profile.id)}
+              disabled={isDownloadingEmbedding || !profile.enabled}
+            />
+            <div className="model-icon">
+              {profile.dtype === 'q8' ? <Zap size={24} /> : <Layers size={24} />}
+            </div>
+            <div className="model-info">
+              <div className="model-header">
+                <span className="model-name">{profile.displayName}</span>
+                <span className="model-size">{profile.sizeLabel}</span>
+              </div>
+              <span className="model-desc">{profile.enabled ? profile.description : '即将支持'}</span>
+            </div>
+            {embeddingProfileId === profile.id && <div className="model-check"><Check size={14} /></div>}
+          </label>
+        ))}
+      </div>
+
+      <h4 className="subsection-title semantic-vector-subtitle">向量计算模式</h4>
+      <div className="model-type-grid semantic-device-grid">
+        <label className={`model-card ${embeddingDevice === 'cpu' ? 'active' : ''} ${isDownloadingEmbedding ? 'disabled' : ''}`}>
+          <input
+            type="radio"
+            name="aiEmbeddingDevice"
+            value="cpu"
+            checked={embeddingDevice === 'cpu'}
+            onChange={() => handleEmbeddingDeviceChange('cpu')}
+            disabled={isDownloadingEmbedding}
+          />
+          <div className="model-icon"><Cpu size={24} /></div>
+          <div className="model-info">
+            <div className="model-header">
+              <span className="model-name">CPU</span>
+              <span className="model-size">稳定</span>
+            </div>
+            <span className="model-desc">默认模式，兼容性最好，适合后台稳定索引。</span>
+          </div>
+          {embeddingDevice === 'cpu' && <div className="model-check"><Check size={14} /></div>}
+        </label>
+
+        <label className={`model-card ${embeddingDevice === 'dml' ? 'active' : ''} ${isDownloadingEmbedding ? 'disabled' : ''}`}>
+          <input
+            type="radio"
+            name="aiEmbeddingDevice"
+            value="dml"
+            checked={embeddingDevice === 'dml'}
+            onChange={() => handleEmbeddingDeviceChange('dml')}
+            disabled={isDownloadingEmbedding}
+          />
+          <div className="model-icon"><Zap size={24} /></div>
+          <div className="model-info">
+            <div className="model-header">
+              <span className="model-name">GPU DirectML</span>
+              <span className="model-size">实验</span>
+            </div>
+            <span className="model-desc">Windows GPU 加速，失败时自动回退 CPU。</span>
+          </div>
+          {embeddingDevice === 'dml' && <div className="model-check"><Check size={14} /></div>}
+        </label>
+      </div>
+
+      {embeddingDeviceStatus && (
+        <div className="semantic-device-status">
+          <div className={`status-indicator ${embeddingDeviceStatus.effectiveDevice === 'dml' ? 'ready' : 'missing'}`}>
+            {embeddingDeviceStatus.effectiveDevice === 'dml' ? <CheckCircle size={18} /> : <AlertCircle size={18} />}
+            <span>当前计算: {embeddingDeviceStatus.provider}</span>
+          </div>
+          <p>{embeddingDeviceStatus.info}</p>
+        </div>
+      )}
+
+      <div className="stt-model-status semantic-vector-model-status">
+        {embeddingStatus ? (
+          <div className="model-info">
+            <div className={`status-indicator ${embeddingStatus.exists ? 'ready' : 'missing'}`}>
+              {embeddingStatus.exists ? (
+                <>
+                  <CheckCircle size={20} />
+                  <span>语义模型已就绪</span>
+                </>
+              ) : (
+                <>
+                  <AlertCircle size={20} />
+                  <span>语义模型未下载</span>
+                </>
+              )}
+            </div>
+            <p className="model-size">
+              模型大小: {formatBytes(embeddingStatus.sizeBytes)}
+              <span> · {embeddingStatus.dim || embeddingProfile?.dim || 1024} 维</span>
+            </p>
+            <p className="model-path">模型目录: {embeddingStatus.modelDir}</p>
+          </div>
+        ) : (
+          <p>正在检查模型状态...</p>
+        )}
+      </div>
+
+      {isDownloadingEmbedding && (
+        <div className="download-progress semantic-download-progress">
+          <div className="progress-bar">
+            <div className="progress-fill" style={{ width: `${Math.max(3, Math.min(100, embeddingProgressPercent))}%` }} />
+          </div>
+          <span className="progress-text">
+            {embeddingProgress?.percent !== undefined
+              ? `${embeddingProgress.percent.toFixed(1)}%`
+              : (embeddingProgress?.remoteHost ? `连接 ${embeddingProgress.remoteHost}` : '准备中')}
+          </span>
+        </div>
+      )}
+
+      <div className="btn-row semantic-vector-actions">
+        {!embeddingStatus?.exists && (
+          <button
+            className="btn btn-primary"
+            onClick={handleDownloadEmbeddingModel}
+            disabled={isDownloadingEmbedding || embeddingProfile?.enabled === false}
+          >
+            <Download size={16} /> {isDownloadingEmbedding ? '下载中...' : '下载模型'}
+          </button>
+        )}
+        {embeddingStatus?.exists && (
+          <button
+            className="btn btn-danger"
+            onClick={async () => {
+              const modelSize = embeddingStatus.sizeLabel || embeddingProfile?.sizeLabel || '约 100 MB'
+              if (confirm(`确定要清除语义向量模型吗？下次使用需要重新下载 (${modelSize})。`)) {
+                await handleClearEmbeddingModel()
+              }
+            }}
+            disabled={isClearingEmbedding}
+          >
+            <Trash2 size={16} /> 清除模型
+          </button>
+        )}
+        <button
+          className="btn btn-secondary"
+          onClick={() => loadEmbeddingStatus(embeddingProfileId)}
+          disabled={isDownloadingEmbedding}
+        >
+          <RefreshCw size={16} className={isDownloadingEmbedding ? 'spin' : ''} /> 刷新状态
+        </button>
+        <button
+          className="btn btn-secondary"
+          onClick={handleClearSemanticIndex}
+        >
+          <Database size={16} /> 清理语义索引
+        </button>
+      </div>
+
       {/* 3. 摘要偏好 */}
       <h3 className="section-title">摘要详细程度</h3>
       <div className="detail-options">
diff --git a/src/pages/AISummaryWindow.tsx b/src/pages/AISummaryWindow.tsx
index ed08c53..109e077 100644
--- a/src/pages/AISummaryWindow.tsx
+++ b/src/pages/AISummaryWindow.tsx
@@ -1410,8 +1410,8 @@ function AISummaryWindow() {
       status: 'running',
       processedCount: vectorConfirm?.state?.vectorizedCount || 0,
       totalCount: vectorConfirm?.state?.indexedCount || 0,
-      message: '正在准备本地向量索引',
-      vectorModel: vectorConfirm?.state?.vectorModel || 'local-chargram-hash-v1'
+      message: '正在准备本地语义向量索引',
+      vectorModel: vectorConfirm?.state?.vectorModel || 'bge-large-zh-v1.5-int8'
     })
 
     try {
@@ -1422,7 +1422,7 @@ function AISummaryWindow() {
 
       const result = await window.electronAPI.ai.prepareSessionVectorIndex({ sessionId })
       if (!result.success || !result.result) {
-        throw new Error(result.error || '向量索引准备失败')
+        throw new Error(result.error || '语义向量索引准备失败')
       }
 
       if (result.result.isVectorComplete) {
@@ -1431,7 +1431,7 @@ function AISummaryWindow() {
         setIsVectorIndexing(false)
         await runAskQuestion(question)
       } else {
-        setQaError('本地向量索引未完成，已取消本次准备')
+        setQaError('本地语义向量索引未完成，已取消本次准备')
       }
     } catch (e) {
       setQaError(String(e))
@@ -1554,6 +1554,7 @@ function AISummaryWindow() {
       ? Math.min(100, Math.round((processedCount / totalCount) * 100))
       : 0
     const pendingCount = Math.max(0, totalCount - processedCount)
+    const isDownloadingModel = vectorProgress?.stage === 'downloading_model'
 
     return (
       <div
@@ -1567,17 +1568,24 @@ function AISummaryWindow() {
       >
         <div className="dialog-box vector-index-dialog" onClick={(e) => e.stopPropagation()}>
           <div className="dialog-header">
-            <h3>准备本地向量索引</h3>
+            <h3>准备本地语义向量索引</h3>
           </div>
           <div className="dialog-content">
             <div className="vector-index-intro">
               <Atom size={18} />
-              <p>当前会话尚未完成本地向量化。建立后，本次和后续问 AI 会优先使用本地相似度检索，新消息会自动增量处理。</p>
+              <p>当前会话尚未完成本地语义向量化。建立后，本次和后续问 AI 会优先使用真实语义检索，新消息会自动增量处理。</p>
             </div>
 
             <div className="vector-index-stats">
-              <span>已向量化 {processedCount} 条</span>
-              <span>待处理 {pendingCount} 条</span>
+              <span>{vectorConfirm.state?.vectorModelName || vectorProgress?.vectorModel || 'BGE Small 中文'}</span>
+              {isDownloadingModel ? (
+                <span>正在下载语义模型</span>
+              ) : (
+                <>
+                  <span>已语义向量化 {processedCount} 条</span>
+                  <span>待处理 {pendingCount} 条</span>
+                </>
+              )}
             </div>
 
             {isVectorIndexing && (
@@ -1601,7 +1609,7 @@ function AISummaryWindow() {
               onClick={handlePrepareVectorIndex}
               disabled={isVectorIndexing}
             >
-              {isVectorIndexing ? '处理中' : '开始向量化'}
+              {isVectorIndexing ? '处理中' : '开始语义向量化'}
             </button>
           </div>
         </div>
@@ -1745,7 +1753,7 @@ function AISummaryWindow() {
             </div>
           )}
           {isVectorIndexing && (
-            <div className="generating-status" data-tooltip="正在准备本地向量索引...">
+            <div className="generating-status" data-tooltip="正在准备本地语义向量索引...">
               <Loader2 className="spinner" size={16} />
             </div>
           )}
diff --git a/src/services/config.ts b/src/services/config.ts
index c633fdd..a300ad3 100644
--- a/src/services/config.ts
+++ b/src/services/config.ts
@@ -44,7 +44,9 @@ export const CONFIG_KEYS = {
   AUTH_CREDENTIAL_ID: 'authCredentialId',
   AUTH_PASSWORD_HASH: 'authPasswordHash',
   AUTH_PASSWORD_SALT: 'authPasswordSalt',
-  CLOSE_TO_TRAY: 'closeToTray'
+  CLOSE_TO_TRAY: 'closeToTray',
+  AI_EMBEDDING_MODEL_PROFILE: 'aiEmbeddingModelProfile',
+  AI_EMBEDDING_DEVICE: 'aiEmbeddingDevice'
 } as const
 
 export type { AccountProfile, AccountProfileInput, AccountProfilePatch }
@@ -628,6 +630,24 @@ export async function setAiMessageLimit(limit: number): Promise<void> {
   await config.set('aiMessageLimit', limit)
 }
 
+export async function getAiEmbeddingModelProfile(): Promise<string> {
+  const value = await config.get(CONFIG_KEYS.AI_EMBEDDING_MODEL_PROFILE)
+  return (value as string) || 'bge-large-zh-v1.5-int8'
+}
+
+export async function setAiEmbeddingModelProfile(profileId: string): Promise<void> {
+  await config.set(CONFIG_KEYS.AI_EMBEDDING_MODEL_PROFILE, profileId || 'bge-large-zh-v1.5-int8')
+}
+
+export async function getAiEmbeddingDevice(): Promise<'cpu' | 'dml'> {
+  const value = await config.get(CONFIG_KEYS.AI_EMBEDDING_DEVICE)
+  return value === 'dml' ? 'dml' : 'cpu'
+}
+
+export async function setAiEmbeddingDevice(device: 'cpu' | 'dml'): Promise<void> {
+  await config.set(CONFIG_KEYS.AI_EMBEDDING_DEVICE, device === 'dml' ? 'dml' : 'cpu')
+}
+
 // --- MCP 配置 ---
 
 export async function getMcpEnabled(): Promise<boolean> {
diff --git a/src/types/ai.ts b/src/types/ai.ts
index 9909a68..192a304 100644
--- a/src/types/ai.ts
+++ b/src/types/ai.ts
@@ -182,10 +182,14 @@ export interface SessionVectorIndexState {
   isVectorComplete: boolean
   isVectorRunning: boolean
   vectorModel: string
+  vectorModelName?: string
+  vectorProviderAvailable?: boolean
+  vectorProviderError?: string
 }
 
 export type SessionVectorIndexProgressStage =
   | 'preparing'
+  | 'downloading_model'
   | 'indexing_messages'
   | 'vectorizing_messages'
   | 'completed'
@@ -206,6 +210,56 @@ export interface SessionVectorIndexProgressEvent {
   vectorModel: string
 }
 
+export interface EmbeddingModelProfile {
+  id: string
+  displayName: string
+  description: string
+  modelId: string
+  remoteHosts: string[]
+  remotePathTemplate: string
+  revision: string
+  dim: number
+  maxTokens: number
+  maxTextChars: number
+  dtype: string
+  sizeLabel: string
+  enabled: boolean
+}
+
+export interface EmbeddingModelStatus {
+  profileId: string
+  displayName: string
+  modelId: string
+  dim: number
+  dtype: string
+  sizeLabel: string
+  enabled: boolean
+  exists: boolean
+  modelDir: string
+  sizeBytes: number
+}
+
+export type EmbeddingDevice = 'cpu' | 'dml'
+
+export interface EmbeddingDeviceStatus {
+  currentDevice: EmbeddingDevice
+  effectiveDevice: EmbeddingDevice
+  gpuAvailable: boolean
+  provider: 'CPU' | 'DirectML'
+  info: string
+}
+
+export interface EmbeddingModelDownloadProgress {
+  profileId: string
+  displayName: string
+  remoteHost?: string
+  file?: string
+  loaded?: number
+  total?: number
+  percent?: number
+  status?: string
+}
+
 export interface SessionQAResult {
   sessionId: string
   question: string
diff --git a/src/types/electron.d.ts b/src/types/electron.d.ts
index 2d3171b..bb45e01 100644
--- a/src/types/electron.d.ts
+++ b/src/types/electron.d.ts
@@ -1,5 +1,10 @@
 import type { ChatSession, Message, Contact, ContactInfo } from './models'
 import type {
+  EmbeddingDevice,
+  EmbeddingDeviceStatus,
+  EmbeddingModelDownloadProgress,
+  EmbeddingModelProfile,
+  EmbeddingModelStatus,
   SessionQAHistoryMessage,
   SessionQAProgressEvent,
   SessionQAResult,
@@ -1089,10 +1094,53 @@ export interface ElectronAPI {
       result?: SessionVectorIndexState
       error?: string
     }>
+    getEmbeddingModelProfiles: () => Promise<{
+      success: boolean
+      result?: EmbeddingModelProfile[]
+      currentProfileId?: string
+      error?: string
+    }>
+    setEmbeddingModelProfile: (profileId: string) => Promise<{
+      success: boolean
+      result?: string
+      error?: string
+    }>
+    getEmbeddingDeviceStatus: () => Promise<{
+      success: boolean
+      result?: EmbeddingDeviceStatus
+      error?: string
+    }>
+    setEmbeddingDevice: (device: EmbeddingDevice) => Promise<{
+      success: boolean
+      result?: EmbeddingDevice
+      status?: EmbeddingDeviceStatus
+      error?: string
+    }>
+    getEmbeddingModelStatus: (profileId?: string) => Promise<{
+      success: boolean
+      result?: EmbeddingModelStatus
+      error?: string
+    }>
+    downloadEmbeddingModel: (profileId?: string) => Promise<{
+      success: boolean
+      result?: EmbeddingModelStatus
+      error?: string
+    }>
+    clearEmbeddingModel: (profileId?: string) => Promise<{
+      success: boolean
+      result?: EmbeddingModelStatus
+      error?: string
+    }>
+    clearSemanticVectorIndex: (vectorModel?: string) => Promise<{
+      success: boolean
+      result?: { success: boolean; deletedCount: number; vectorModel: string }
+      error?: string
+    }>
     onSummaryChunk: (callback: (chunk: string) => void) => () => void
     onSessionQAChunk: (callback: (chunk: string) => void) => () => void
     onSessionQAProgress: (callback: (event: SessionQAProgressEvent) => void) => () => void
     onSessionVectorIndexProgress: (callback: (event: SessionVectorIndexProgressEvent) => void) => () => void
+    onEmbeddingModelDownloadProgress: (callback: (event: EmbeddingModelDownloadProgress) => void) => () => void
   }
 
 }