add local embed

package.json

@@ -61,6 +61,7 @@
 		"@electric-sql/pglite": "0.2.14",
 		"@google/genai": "^1.2.0",
 		"@google/generative-ai": "^0.21.0",
+		"@huggingface/transformers": "^3.6.1",
 		"@langchain/core": "^0.3.26",
 		"@lexical/clipboard": "^0.17.1",
 		"@lexical/react": "^0.17.1",
@@ -74,6 +75,7 @@
 		"@radix-ui/react-tooltip": "^1.1.3",
 		"@tanstack/react-query": "^5.56.2",
 		"@types/mermaid": "^9.2.0",
+		"@xenova/transformers": "^2.17.2",
 		"axios": "^1.8.3",
 		"chokidar": "^4.0.3",
 		"clsx": "^2.1.1",
@@ -119,6 +121,7 @@
 		"remark-gfm": "^4.0.0",
 		"shell-env": "^4.0.1",
 		"simple-git": "^3.27.0",
+		"smart-embed-model": "^1.0.7",
 		"string-similarity": "^4.0.4",
 		"styled-components": "^6.1.19",
 		"uuid": "^10.0.0",

src/components/chat-view/ChatHistoryView.tsx

@@ -1,7 +1,8 @@
-import { CheckSquare, Clock, CopyPlus, MessageSquare, Pencil, Search, Sparkles, Square, Trash2 } from 'lucide-react'
+import { CheckSquare, Clock, CopyPlus, Globe, MessageSquare, Pencil, Search, Sparkles, Square, Trash2 } from 'lucide-react'
 import { Notice } from 'obsidian'
 import React, { useMemo, useRef, useState } from 'react'
 
+import { useSettings } from '../../contexts/SettingsContext'
 import { useChatHistory } from '../../hooks/use-chat-history'
 import { t } from '../../lang/helpers'
 import { ChatConversationMeta } from '../../types/chat'
@@ -29,6 +30,15 @@ const ChatHistoryView = ({
 	// search term
 	const [searchTerm, setSearchTerm] = useState('')
 
+	const { settings } = useSettings()
+
+	const currentWorkspace = React.useMemo(() => {
+		return settings.workspace || 'vault'
+	}, [settings.workspace])
+
+	// workspace filter state
+	const [filterByWorkspace, setFilterByWorkspace] = useState(false)
+
 	// editing conversation id
 	const [editingConversationId, setEditingConversationId] = useState<string | null>(null)
 
@@ -62,16 +72,33 @@ const ChatHistoryView = ({
 		setSearchTerm(e.target.value)
 	}
 
+	// toggle workspace filter
+	const toggleWorkspaceFilter = () => {
+		setFilterByWorkspace(!filterByWorkspace)
+	}
+
 	// filter conversations list
 	const filteredConversations = useMemo(() => {
-		if (!searchTerm.trim()) {
+		console.log('filteredConversations', chatList)
-			return chatList
+		let filtered = chatList
-		}
+
-		return chatList.filter(
+		// Apply search filter
+		if (searchTerm.trim()) {
+			filtered = filtered.filter(
 				conversation =>
 					conversation.title.toLowerCase().includes(searchTerm.toLowerCase())
 			)
-	}, [chatList, searchTerm])
+		}
+
+		// Apply workspace filter
+		if (filterByWorkspace) {
+			filtered = filtered.filter(
+				conversation => conversation.workspace === currentWorkspace
+			)
+		}
+
+		return filtered
+	}, [chatList, searchTerm, filterByWorkspace, currentWorkspace])
 
 	// toggle selection mode
 	const toggleSelectionMode = () => {
@@ -285,6 +312,18 @@ const ChatHistoryView = ({
 				/>
 			</div>
 
+			{/* workspace filter */}
+			<div className="infio-chat-history-workspace-filter">
+				<button
+					onClick={toggleWorkspaceFilter}
+					className={`infio-chat-history-workspace-filter-btn ${filterByWorkspace ? 'active' : ''}`}
+					title={filterByWorkspace ? '显示所有对话' : '只显示当前工作区对话'}
+				>
+					<Globe size={14} />
+					当前工作区
+				</button>
+			</div>
+
 			{/* conversations list */}
 			<div className="infio-chat-history-list">
 				{filteredConversations.length === 0 ? (
@@ -353,6 +392,11 @@ const ChatHistoryView = ({
 											{formatDate(conversation.updatedAt)}
 										</div>
 										<div className="infio-chat-history-conversation-title">{conversation.title}</div>
+										{conversation.workspace && (
+											<div className="infio-chat-history-workspace">
+												工作区: {conversation.workspace}
+											</div>
+										)}
 									</div>
 									{!selectionMode && (
 										<div className="infio-chat-history-actions">
@@ -426,6 +470,7 @@ const ChatHistoryView = ({
 					flex-shrink: 0;
 				}
 
+				.infio-chat-history-filter-btn,
 				.infio-chat-history-cleanup-btn,
 				.infio-chat-history-selection-btn {
 					display: flex !important;
@@ -444,12 +489,14 @@ const ChatHistoryView = ({
 					box-sizing: border-box;
 				}
 
+				.infio-chat-history-filter-btn:hover,
 				.infio-chat-history-cleanup-btn:hover,
 				.infio-chat-history-selection-btn:hover {
 					background-color: var(--background-modifier-hover, #f5f5f5);
 					border-color: var(--background-modifier-border-hover, #d0d0d0);
 				}
 
+				.infio-chat-history-filter-btn.active,
 				.infio-chat-history-selection-btn.active {
 					background-color: var(--interactive-accent, #007acc);
 					color: var(--text-on-accent, #ffffff);
@@ -539,7 +586,6 @@ const ChatHistoryView = ({
 					border: 1px solid var(--background-modifier-border);
 					border-radius: var(--radius-s);
 					padding: 6px 12px;
-					margin-bottom: var(--size-4-3);
 					transition: all 0.2s ease;
 					height: 36px;
 					max-width: 100%;
@@ -675,6 +721,13 @@ const ChatHistoryView = ({
 					font-size: 12px;
 				}
 
+				.infio-chat-history-workspace {
+					color: var(--text-muted);
+					font-size: 11px;
+					margin-top: 2px;
+					opacity: 0.8;
+				}
+
 				.infio-chat-history-actions {
 					display: flex;
 					gap: 4px;
@@ -768,6 +821,38 @@ const ChatHistoryView = ({
 				.infio-chat-history-cancel-btn:hover {
 					background-color: var(--background-modifier-hover);
 				}
+
+				.infio-chat-history-workspace-filter {
+					display: flex;
+					justify-content: flex-start;
+					align-items: center;
+					margin-bottom: 12px;
+				}
+
+				.infio-chat-history-workspace-filter-btn {
+					display: flex;
+					align-items: center;
+					gap: 6px;
+					background-color: transparent;
+					border: 1px solid var(--background-modifier-border);
+					color: var(--text-muted);
+					padding: 6px 12px;
+					border-radius: var(--radius-s);
+					cursor: pointer;
+					font-size: var(--font-ui-small);
+					transition: all 0.2s ease;
+				}
+
+				.infio-chat-history-workspace-filter-btn:hover {
+					background-color: var(--background-modifier-hover);
+					color: var(--text-normal);
+				}
+
+				.infio-chat-history-workspace-filter-btn.active {
+					background-color: var(--interactive-accent);
+					color: var(--text-on-accent);
+					border-color: var(--interactive-accent);
+				}
 				`}
 			</style>
 		</div>

src/components/chat-view/ChatView.tsx

@@ -879,7 +879,7 @@ const Chat = forwardRef<ChatRef, ChatProps>((props, ref) => {
 						// Execute the transformation using the TransEngine
 						const transformationResult = await transEngine.runTransformation({
 							filePath: toolArgs.path,
-							transformationType: transformationType,
+							transformationType: transformationType as TransformationType,
 							model: {
 								provider: settings.applyModelProvider,
 								modelId: settings.applyModelId,

src/components/chat-view/SearchView.tsx

@@ -148,7 +148,7 @@ const SearchView = () => {
 					minSimilarity: 0.3,
 				})
 				
-				setInsightResults(results)
+				setInsightResults(results as any)
 				setSearchResults([])
 			}
 		} catch (error) {

src/components/chat-view/WorkspaceEditModal.tsx

@@ -60,7 +60,8 @@ const WorkspaceEditModal = ({
 
       // 直接使用 Obsidian 的内置接口获取所有标签
 			try {
-				const tagsObject = app.metadataCache.getTags() // 获取所有标签 {'#tag1': 2, '#tag2': 4}
+				// @ts-ignore
+				const tagsObject = app.metadataCache.getTags() as Record<string, number> // 获取所有标签 {'#tag1': 2, '#tag2': 4}
         const tags = Object.keys(tagsObject).sort()
         setAvailableTags(tags)
       } catch (error) {

src/core/prompts/sections/rules.ts

@@ -112,5 +112,5 @@ export function getRulesSection(
 	if (mode === 'learn') {
 		return getLearnModeRulesSection(cwd, searchTool);
 	}
-	return getObsidianRulesSection(mode, cwd, searchTool, supportsComputerUse, diffStrategy, experiments);
+	return getObsidianRulesSection(mode, cwd, searchTool);
 }

src/core/prompts/sections/tool-use-guidelines.ts

@@ -21,8 +21,5 @@ By waiting for and carefully considering the user's response after each tool use
 }
 
 export function getToolUseGuidelinesSection(mode?: string): string {
-	if (mode === 'learn') {
-		return getLearnModeToolUseGuidelines();
-	}
 	return getDefaultToolUseGuidelines();
 }

src/core/prompts/tools/tool-groups.ts

@@ -61,7 +61,7 @@ export type ToolName = keyof typeof TOOL_DISPLAY_NAMES
 
 // Tool helper functions
 export function getToolName(toolConfig: string | readonly [ToolName, ...unknown[]]): ToolName {
-	return typeof toolConfig === "string" ? toolConfig : toolConfig[0]
+	return typeof toolConfig === "string" ? toolConfig as ToolName : toolConfig[0]
 }
 
 export function getToolOptions(toolConfig: string | readonly [ToolName, ...unknown[]]): unknown {

src/core/transformations/trans-engine.ts

@@ -981,8 +981,9 @@ export class TransEngine {
 				}
 				break;
 			}
-
+			case TransformationType.DENSE_SUMMARY:
-			case TransformationType.CONCISE_DENSE_SUMMARY:
+				// 新的摘要类型不需要特殊的后处理，保持原样
+				break;
 			case TransformationType.HIERARCHICAL_SUMMARY:
 				// 新的摘要类型不需要特殊的后处理，保持原样
 				break;
@@ -1237,7 +1238,7 @@ export class TransEngine {
 			const cacheResult = await this.checkDatabaseCache(
 				fileMetadata.sourcePath,
 				fileMetadata.sourceMtime,
-				TransformationType.CONCISE_DENSE_SUMMARY
+				TransformationType.DENSE_SUMMARY
 			)
 
 			if (cacheResult.foundCache && cacheResult.result.success && cacheResult.result.result) {
@@ -1273,7 +1274,7 @@ export class TransEngine {
 			// 保存到数据库
 			await this.saveResultToDatabase(
 				summary,
-				TransformationType.CONCISE_DENSE_SUMMARY,
+				TransformationType.DENSE_SUMMARY,
 				fileMetadata.sourcePath,
 				fileMetadata.sourceMtime,
 				'document'
@@ -1295,7 +1296,7 @@ export class TransEngine {
 		const messages: RequestMessage[] = [
 			{
 				role: 'system',
-				content: CONCISE_DENSE_SUMMARY_PROMPT
+				content: DENSE_SUMMARY_PROMPT
 			},
 			{
 				role: 'user',
@@ -1308,7 +1309,7 @@ export class TransEngine {
 			throw new Error(`生成摘要失败: ${result.error.message}`)
 		}
 
-		return this.postProcessResult(result.value, TransformationType.CONCISE_DENSE_SUMMARY)
+		return this.postProcessResult(result.value, TransformationType.DENSE_SUMMARY)
 	}
 
 	/**

src/database/json/chat/ChatManager.ts

@@ -5,6 +5,7 @@ import { ChatConversationMeta } from '../../../types/chat'
 import { AbstractJsonRepository } from '../base'
 import { CHAT_DIR, ROOT_DIR } from '../constants'
 import { EmptyChatTitleException } from '../exception'
+import { WorkspaceManager } from '../workspace/WorkspaceManager'
 
 import {
 	CHAT_SCHEMA_VERSION,
@@ -16,27 +17,34 @@ export class ChatManager extends AbstractJsonRepository<
 	ChatConversation,
 	ChatConversationMeta
 > {
-	constructor(app: App) {
+	private workspaceManager?: WorkspaceManager
+
+	constructor(app: App, workspaceManager?: WorkspaceManager) {
 		super(app, `${ROOT_DIR}/${CHAT_DIR}`)
+		this.workspaceManager = workspaceManager
 	}
 
 	protected generateFileName(chat: ChatConversation): string {
-		// Format: v{schemaVersion}_{title}_{updatedAt}_{id}.json
+		// 新格式: v{schemaVersion}_{title}_{updatedAt}_{id}_{workspaceId}.json
+		// 如果没有工作区，使用 'vault' 作为默认值
 		const encodedTitle = encodeURIComponent(chat.title)
-		return `v${chat.schemaVersion}_${encodedTitle}_${chat.updatedAt}_${chat.id}.json`
+		const workspaceId = chat.workspace || 'vault'
+		return `v${chat.schemaVersion}_${encodedTitle}_${chat.updatedAt}_${chat.id}_${workspaceId}.json`
 	}
 
 	protected parseFileName(fileName: string): ChatConversationMeta | null {
-		// Parse: v{schemaVersion}_{title}_{updatedAt}_{id}.json
+		// 使用一个正则表达式，工作区部分为可选: v{schemaVersion}_{title}_{updatedAt}_{id}_{workspaceId}?.json
 		const regex = new RegExp(
-			`^v${CHAT_SCHEMA_VERSION}_(.+)_(\\d+)_([0-9a-f-]+)\\.json$`,
+			`^v${CHAT_SCHEMA_VERSION}_(.+)_(\\d+)_([0-9a-f-]+)(?:_([^_]+))?\\.json$`,
 		)
 		const match = fileName.match(regex)
+
 		if (!match) return null
 
 		const title = decodeURIComponent(match[1])
 		const updatedAt = parseInt(match[2], 10)
 		const id = match[3]
+		const workspaceId = match[4] // 可能为undefined（老格式）
 
 		return {
 			id,
@@ -44,6 +52,8 @@ export class ChatManager extends AbstractJsonRepository<
 			title,
 			updatedAt,
 			createdAt: 0,
+			// 如果没有工作区信息（老格式），则认为是vault（全局消息）
+			workspace: workspaceId === 'vault' ? undefined : workspaceId,
 		}
 	}
 
@@ -66,6 +76,20 @@ export class ChatManager extends AbstractJsonRepository<
 		}
 
 		await this.create(newChat)
+
+		// 如果有工作区信息，添加到工作区的聊天历史中
+		if (newChat.workspace && this.workspaceManager) {
+			try {
+				await this.workspaceManager.addChatToWorkspace(
+					newChat.workspace,
+					newChat.id,
+					newChat.title
+				)
+			} catch (error) {
+				console.error('Failed to add chat to workspace:', error)
+			}
+		}
+
 		return newChat
 	}
 
@@ -102,6 +126,23 @@ export class ChatManager extends AbstractJsonRepository<
 		}
 
 		await this.update(chat, updatedChat)
+
+		// 如果标题或工作区发生变化，更新工作区的聊天历史
+		if (this.workspaceManager && (updates.title !== undefined || updates.workspace !== undefined)) {
+			const workspaceId = updatedChat.workspace || chat.workspace
+			if (workspaceId) {
+				try {
+					await this.workspaceManager.addChatToWorkspace(
+						workspaceId,
+						updatedChat.id,
+						updatedChat.title
+					)
+				} catch (error) {
+					console.error('Failed to update chat in workspace:', error)
+				}
+			}
+		}
+
 		return updatedChat
 	}
 
@@ -111,9 +152,22 @@ export class ChatManager extends AbstractJsonRepository<
 
 		if (targetsToDelete.length === 0) return false
 
+		// 获取聊天的工作区信息（从第一个匹配的元数据中获取）
+		const chatToDelete = await this.findById(id)
+		const workspaceId = chatToDelete?.workspace
+
 		// Delete all files associated with this ID
 		await Promise.all(targetsToDelete.map(meta => this.delete(meta.fileName)))
 
+		// 从工作区的聊天历史中移除
+		if (workspaceId && this.workspaceManager) {
+			try {
+				await this.workspaceManager.removeChatFromWorkspace(workspaceId, id)
+			} catch (error) {
+				console.error('Failed to remove chat from workspace:', error)
+			}
+		}
+
 		return true
 	}
 
@@ -126,7 +180,10 @@ export class ChatManager extends AbstractJsonRepository<
 			if (!chatsById.has(meta.id)) {
 				chatsById.set(meta.id, [])
 			}
-			chatsById.get(meta.id)!.push(meta)
+			const chatGroup = chatsById.get(meta.id)
+			if (chatGroup) {
+				chatGroup.push(meta)
+			}
 		}
 
 		const filesToDelete: string[] = []
@@ -151,11 +208,12 @@ export class ChatManager extends AbstractJsonRepository<
 		return filesToDelete.length
 	}
 
-	public async listChats(): Promise<ChatConversationMeta[]> {
+	public async listChats(workspaceFilter?: string): Promise<ChatConversationMeta[]> {
+		console.log('listChats', workspaceFilter)
 		const metadata = await this.listMetadata()
 
 		// Use a Map to store the latest version of each chat by ID.
-		const latestChats = new Map<string, ChatConversationMeta>()
+		const latestChats = new Map<string, ChatConversationMeta & { fileName: string }>()
 
 		for (const meta of metadata) {
 			const existing = latestChats.get(meta.id)
@@ -165,7 +223,27 @@ export class ChatManager extends AbstractJsonRepository<
 		}
 
 		const uniqueMetadata = Array.from(latestChats.values())
-		const sorted = uniqueMetadata.sort((a, b) => b.updatedAt - a.updatedAt)
+
+		// 将metadata转换为ChatConversationMeta格式
+		const chatMetadata: ChatConversationMeta[] = uniqueMetadata.map((meta) => ({
+			id: meta.id,
+			schemaVersion: meta.schemaVersion,
+			title: meta.title,
+			updatedAt: meta.updatedAt,
+			createdAt: meta.createdAt,
+			workspace: meta.workspace
+		}))
+
+		// 如果指定了工作区过滤器，则过滤对话
+		let filteredMetadata = chatMetadata
+		if (workspaceFilter !== undefined && workspaceFilter !== 'vault') {
+			// 获取指定工作区的对话
+			filteredMetadata = chatMetadata.filter(meta =>
+				meta.workspace === workspaceFilter
+			)
+		}
+
+		const sorted = filteredMetadata.sort((a, b) => b.updatedAt - a.updatedAt)
 		return sorted
 	}
 }

src/database/json/chat/types.ts

@@ -9,6 +9,7 @@ export type ChatConversation = {
   createdAt: number
   updatedAt: number
   schemaVersion: number
+  workspace?: string // 工作区ID，可选字段用于向后兼容
 }
 
 export type ChatConversationMetadata = {
@@ -16,4 +17,5 @@ export type ChatConversationMetadata = {
   title: string
   updatedAt: number
   schemaVersion: number
+  workspace?: string // 工作区ID，可选字段用于向后兼容
 }

src/database/json/workspace/WorkspaceManager.ts

@@ -118,11 +118,11 @@ export class WorkspaceManager extends AbstractJsonRepository<
 	}
 
 	public async addChatToWorkspace(
-    workspaceId: string,
+		workspaceName: string,
 		chatId: string,
 		chatTitle: string
 	): Promise<Workspace | null> {
-    const workspace = await this.findById(workspaceId)
+		const workspace = await this.findByName(workspaceName)
 		if (!workspace) return null
 
 		const existingChatIndex = workspace.chatHistory.findIndex(
@@ -137,7 +137,7 @@ export class WorkspaceManager extends AbstractJsonRepository<
 			workspace.chatHistory.push({ id: chatId, title: chatTitle })
 		}
 
-    return this.updateWorkspace(workspaceId, {
+		return this.updateWorkspace(workspace.id, {
 			chatHistory: workspace.chatHistory
 		})
 	}

src/embedworker/EmbeddingManager.ts

@@ -0,0 +1,274 @@
+// 导入完整的嵌入 Worker
+// @ts-nocheck
+import EmbedWorker from './embed.worker';
+
+// 类型定义
+export interface EmbedResult {
+    vec: number[];
+    tokens: number;
+    embed_input?: string;
+}
+
+export interface ModelLoadResult {
+    model_loaded: boolean;
+}
+
+export interface ModelUnloadResult {
+    model_unloaded: boolean;
+}
+
+export interface TokenCountResult {
+    tokens: number;
+}
+
+export class EmbeddingManager {
+    private worker: Worker;
+    private requests = new Map<number, { resolve: (value: any) => void; reject: (reason?: any) => void }>();
+    private nextRequestId = 0;
+    private isModelLoaded = false;
+    private currentModelId: string | null = null;
+
+    constructor() {
+        // 创建 Worker，使用与 pgworker 相同的模式
+        this.worker = new EmbedWorker();
+
+        // 统一监听来自 Worker 的所有消息
+        this.worker.onmessage = (event) => {
+            const { id, result, error } = event.data;
+
+            // 根据返回的 id 找到对应的 Promise 回调
+            const request = this.requests.get(id);
+
+            if (request) {
+                if (error) {
+                    request.reject(new Error(error));
+                } else {
+                    request.resolve(result);
+                }
+                // 完成后从 Map 中删除
+                this.requests.delete(id);
+            }
+        };
+
+        this.worker.onerror = (error) => {
+            console.error("EmbeddingWorker error:", error);
+            // 拒绝所有待处理的请求
+            this.requests.forEach(request => {
+                request.reject(error);
+            });
+            this.requests.clear();
+        };
+    }
+
+
+
+    /**
+     * 向 Worker 发送一个请求，并返回一个 Promise，该 Promise 将在收到响应时解析。
+     * @param method 要调用的方法 (e.g., 'load', 'embed_batch')
+     * @param params 方法所需的参数
+     */
+    private postRequest<T>(method: string, params: any): Promise<T> {
+        return new Promise<T>((resolve, reject) => {
+            const id = this.nextRequestId++;
+            this.requests.set(id, { resolve, reject });
+            this.worker.postMessage({ method, params, id });
+        });
+    }
+
+    /**
+     * 加载指定的嵌入模型到 Worker 中。
+     * @param modelId 模型ID, 例如 'TaylorAI/bge-micro-v2'
+     * @param useGpu 是否使用GPU加速，默认为false
+     */
+    public async loadModel(modelId: string, useGpu: boolean = false): Promise<ModelLoadResult> {
+        console.log(`Loading embedding model: ${modelId}, GPU: ${useGpu}`);
+        
+        try {
+            // 如果已经加载了相同的模型，直接返回
+            if (this.isModelLoaded && this.currentModelId === modelId) {
+                console.log(`Model ${modelId} already loaded`);
+                return { model_loaded: true };
+            }
+            
+            // 如果加载了不同的模型，先卸载
+            if (this.isModelLoaded && this.currentModelId !== modelId) {
+                console.log(`Unloading previous model: ${this.currentModelId}`);
+                await this.unloadModel();
+            }
+            
+            const result = await this.postRequest<ModelLoadResult>('load', { 
+                model_key: modelId, 
+                use_gpu: useGpu 
+            });
+            
+            this.isModelLoaded = result.model_loaded;
+            this.currentModelId = result.model_loaded ? modelId : null;
+            
+            if (result.model_loaded) {
+                console.log(`Model ${modelId} loaded successfully`);
+            }
+            
+            return result;
+        } catch (error) {
+            console.error(`Failed to load model ${modelId}:`, error);
+            this.isModelLoaded = false;
+            this.currentModelId = null;
+            throw error;
+        }
+    }
+
+    /**
+     * 为一批文本生成嵌入向量。
+     * @param texts 要处理的文本数组
+     * @returns 返回一个包含向量和 token 信息的对象数组
+     */
+    public async embedBatch(texts: string[]): Promise<EmbedResult[]> {
+        if (!this.isModelLoaded) {
+            throw new Error('Model not loaded. Please call loadModel() first.');
+        }
+        
+        if (!texts || texts.length === 0) {
+            return [];
+        }
+        
+        console.log(`Generating embeddings for ${texts.length} texts`);
+        
+        try {
+            const inputs = texts.map(text => ({ embed_input: text }));
+            const results = await this.postRequest<EmbedResult[]>('embed_batch', { inputs });
+            
+            console.log(`Generated ${results.length} embeddings`);
+            return results;
+        } catch (error) {
+            console.error('Failed to generate embeddings:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * 为单个文本生成嵌入向量。
+     * @param text 要处理的文本
+     * @returns 返回包含向量和 token 信息的对象
+     */
+    public async embed(text: string): Promise<EmbedResult> {
+        if (!text || text.trim().length === 0) {
+            throw new Error('Text cannot be empty');
+        }
+        
+        const results = await this.embedBatch([text]);
+        if (results.length === 0) {
+            throw new Error('Failed to generate embedding');
+        }
+        
+        return results[0];
+    }
+
+    /**
+     * 计算文本的 token 数量。
+     * @param text 要计算的文本
+     */
+    public async countTokens(text: string): Promise<TokenCountResult> {
+        if (!this.isModelLoaded) {
+            throw new Error('Model not loaded. Please call loadModel() first.');
+        }
+        
+        if (!text) {
+            return { tokens: 0 };
+        }
+        
+        try {
+            return await this.postRequest<TokenCountResult>('count_tokens', text);
+        } catch (error) {
+            console.error('Failed to count tokens:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * 卸载模型，释放内存。
+     */
+    public async unloadModel(): Promise<ModelUnloadResult> {
+        if (!this.isModelLoaded) {
+            console.log('No model to unload');
+            return { model_unloaded: true };
+        }
+        
+        try {
+            console.log(`Unloading model: ${this.currentModelId}`);
+            const result = await this.postRequest<ModelUnloadResult>('unload', {});
+            
+            this.isModelLoaded = false;
+            this.currentModelId = null;
+            
+            console.log('Model unloaded successfully');
+            return result;
+        } catch (error) {
+            console.error('Failed to unload model:', error);
+            // 即使卸载失败，也重置状态
+            this.isModelLoaded = false;
+            this.currentModelId = null;
+            throw error;
+        }
+    }
+
+    /**
+     * 检查模型是否已加载。
+     */
+    public get modelLoaded(): boolean {
+        return this.isModelLoaded;
+    }
+
+    /**
+     * 获取当前加载的模型ID。
+     */
+    public get currentModel(): string | null {
+        return this.currentModelId;
+    }
+
+    /**
+     * 获取支持的模型列表。
+     */
+    public getSupportedModels(): string[] {
+        return [
+            'Xenova/all-MiniLM-L6-v2',
+            'Xenova/bge-small-en-v1.5',
+            'Xenova/bge-base-en-v1.5',
+            'Xenova/jina-embeddings-v2-base-zh',
+            'Xenova/jina-embeddings-v2-small-en',
+            'Xenova/multilingual-e5-small',
+            'Xenova/multilingual-e5-base',
+            'Xenova/gte-small',
+            'Xenova/e5-small-v2',
+            'Xenova/e5-base-v2'
+        ];
+    }
+
+    /**
+     * 获取模型信息。
+     */
+    public getModelInfo(modelId: string): { dims: number; maxTokens: number; description: string } | null {
+        const modelInfoMap: Record<string, { dims: number; maxTokens: number; description: string }> = {
+            'Xenova/all-MiniLM-L6-v2': { dims: 384, maxTokens: 512, description: 'All-MiniLM-L6-v2 (推荐，轻量级)' },
+            'Xenova/bge-small-en-v1.5': { dims: 384, maxTokens: 512, description: 'BGE-small-en-v1.5' },
+            'Xenova/bge-base-en-v1.5': { dims: 768, maxTokens: 512, description: 'BGE-base-en-v1.5 (更高质量)' },
+            'Xenova/jina-embeddings-v2-base-zh': { dims: 768, maxTokens: 8192, description: 'Jina-v2-base-zh (中英双语)' },
+            'Xenova/jina-embeddings-v2-small-en': { dims: 512, maxTokens: 8192, description: 'Jina-v2-small-en' },
+            'Xenova/multilingual-e5-small': { dims: 384, maxTokens: 512, description: 'E5-small (多语言)' },
+            'Xenova/multilingual-e5-base': { dims: 768, maxTokens: 512, description: 'E5-base (多语言，更高质量)' },
+            'Xenova/gte-small': { dims: 384, maxTokens: 512, description: 'GTE-small' },
+            'Xenova/e5-small-v2': { dims: 384, maxTokens: 512, description: 'E5-small-v2' },
+            'Xenova/e5-base-v2': { dims: 768, maxTokens: 512, description: 'E5-base-v2 (更高质量)' }
+        };
+
+        return modelInfoMap[modelId] || null;
+    }
+
+    /**
+     * 终止 Worker，释放资源。
+     */
+    public terminate() {
+        this.worker.terminate();
+        this.requests.clear();
+        this.isModelLoaded = false;
+    }
+} 

src/embedworker/README.md

@@ -0,0 +1,171 @@
+# 本地嵌入功能
+
+这个模块提供了在 Web Worker 中运行的本地嵌入功能，使用 Transformers.js 库来生成文本的向量表示。
+
+## 功能特性
+
+- 🚀 **高性能**: 在 Web Worker 中运行，不阻塞主线程
+- 🔒 **隐私保护**: 完全本地运行，数据不离开设备
+- 🎯 **多模型支持**: 支持多种预训练的嵌入模型
+- 💾 **内存管理**: 自动管理模型加载和卸载
+- 🔧 **类型安全**: 完整的 TypeScript 类型支持
+
+## 快速开始
+
+### 基本使用
+
+```typescript
+import { embeddingManager } from './embedworker';
+
+// 加载模型
+await embeddingManager.loadModel('Xenova/all-MiniLM-L6-v2');
+
+// 生成单个文本的嵌入向量
+const result = await embeddingManager.embed('Hello, world!');
+console.log(result.vec); // [0.1234, -0.5678, ...]
+console.log(result.tokens); // 3
+
+// 批量生成嵌入向量
+const texts = ['Hello', 'World', 'AI is amazing'];
+const results = await embeddingManager.embedBatch(texts);
+
+// 计算 token 数量
+const tokenCount = await embeddingManager.countTokens('How many tokens?');
+console.log(tokenCount.tokens); // 4
+```
+
+### 高级使用
+
+```typescript
+import { EmbeddingManager } from './embedworker';
+
+// 创建自定义实例
+const customEmbedding = new EmbeddingManager();
+
+// 使用 GPU 加速（如果支持）
+await customEmbedding.loadModel('Xenova/all-MiniLM-L6-v2', true);
+
+// 检查模型状态
+console.log(customEmbedding.modelLoaded); // true
+console.log(customEmbedding.currentModel); // 'TaylorAI/bge-micro-v2'
+
+// 获取支持的模型列表
+const models = customEmbedding.getSupportedModels();
+console.log(models);
+
+// 获取模型信息
+const modelInfo = customEmbedding.getModelInfo('Xenova/all-MiniLM-L6-v2');
+console.log(modelInfo); // { dims: 384, maxTokens: 512, description: '...' }
+
+// 切换模型
+await customEmbedding.loadModel('Snowflake/snowflake-arctic-embed-xs');
+
+// 清理资源
+await customEmbedding.unloadModel();
+customEmbedding.terminate();
+```
+
+## 支持的模型
+
+| 模型 | 维度 | 最大Token | 描述 |
+|------|------|-----------|------|
+| Xenova/all-MiniLM-L6-v2 | 384 | 512 | All-MiniLM-L6-v2 (推荐，轻量级) |
+| Xenova/bge-small-en-v1.5 | 384 | 512 | BGE-small-en-v1.5 |
+| Xenova/bge-base-en-v1.5 | 768 | 512 | BGE-base-en-v1.5 (更高质量) |
+| Xenova/jina-embeddings-v2-base-zh | 768 | 8192 | Jina-v2-base-zh (中英双语) |
+| Xenova/jina-embeddings-v2-small-en | 512 | 8192 | Jina-v2-small-en |
+| Xenova/multilingual-e5-small | 384 | 512 | E5-small (多语言) |
+| Xenova/multilingual-e5-base | 768 | 512 | E5-base (多语言，更高质量) |
+| Xenova/gte-small | 384 | 512 | GTE-small |
+| Xenova/e5-small-v2 | 384 | 512 | E5-small-v2 |
+| Xenova/e5-base-v2 | 768 | 512 | E5-base-v2 (更高质量) |
+
+## API 参考
+
+### EmbeddingManager
+
+#### 方法
+
+- `loadModel(modelId: string, useGpu?: boolean): Promise<ModelLoadResult>`
+  - 加载指定的嵌入模型
+  - `modelId`: 模型标识符
+  - `useGpu`: 是否使用 GPU 加速（默认 false）
+
+- `embed(text: string): Promise<EmbedResult>`
+  - 为单个文本生成嵌入向量
+  - 返回包含向量和 token 数量的结果
+
+- `embedBatch(texts: string[]): Promise<EmbedResult[]>`
+  - 为多个文本批量生成嵌入向量
+  - 更高效的批处理方式
+
+- `countTokens(text: string): Promise<TokenCountResult>`
+  - 计算文本的 token 数量
+
+- `unloadModel(): Promise<ModelUnloadResult>`
+  - 卸载当前模型，释放内存
+
+- `terminate(): void`
+  - 终止 Worker，释放所有资源
+
+#### 属性
+
+- `modelLoaded: boolean` - 模型是否已加载
+- `currentModel: string | null` - 当前加载的模型ID
+
+#### 工具方法
+
+- `getSupportedModels(): string[]` - 获取支持的模型列表
+- `getModelInfo(modelId: string)` - 获取模型详细信息
+
+### 类型定义
+
+```typescript
+interface EmbedResult {
+    vec: number[];           // 嵌入向量
+    tokens: number;          // token 数量
+    embed_input?: string;    // 原始输入文本
+}
+
+interface ModelLoadResult {
+    model_loaded: boolean;   // 是否加载成功
+}
+
+interface ModelUnloadResult {
+    model_unloaded: boolean; // 是否卸载成功
+}
+
+interface TokenCountResult {
+    tokens: number;          // token 数量
+}
+```
+
+## 错误处理
+
+```typescript
+try {
+    await embeddingManager.loadModel('invalid-model');
+} catch (error) {
+    console.error('加载模型失败:', error.message);
+}
+
+try {
+    const result = await embeddingManager.embed('');
+} catch (error) {
+    console.error('文本不能为空:', error.message);
+}
+```
+
+## 性能考虑
+
+1. **模型加载**: 首次加载模型需要下载和初始化，可能需要几秒到几分钟
+2. **批处理**: 使用 `embedBatch` 比多次调用 `embed` 更高效
+3. **内存使用**: 大模型需要更多内存，注意设备限制
+4. **GPU 加速**: 在支持 WebGPU 的浏览器中可以启用 GPU 加速
+
+## 注意事项
+
+- 首次使用某个模型时需要从 Hugging Face 下载，请确保网络连接正常
+- 模型文件会被浏览器缓存，后续使用会更快
+- 在移动设备上使用大模型可能会遇到内存限制
+- Worker 在后台运行，不会阻塞 UI 线程 

src/embedworker/embed.worker.ts

@@ -0,0 +1,353 @@
+// 完整的嵌入 Worker，使用 Transformers.js
+console.log('Embedding worker loaded');
+
+// 类型定义
+interface EmbedInput {
+    embed_input: string;
+}
+
+interface EmbedResult {
+    vec: number[];
+    tokens: number;
+    embed_input?: string;
+}
+
+interface WorkerMessage {
+    method: string;
+    params: any;
+    id: number;
+    worker_id?: string;
+}
+
+interface WorkerResponse {
+    id: number;
+    result?: any;
+    error?: string;
+    worker_id?: string;
+}
+
+// 全局变量
+let model: any = null;
+let pipeline: any = null;
+let tokenizer: any = null;
+let processing_message = false;
+let transformersLoaded = false;
+
+// 动态导入 Transformers.js
+async function loadTransformers() {
+    if (transformersLoaded) return;
+    
+    try {
+        console.log('Loading Transformers.js...');
+        
+        // 尝试使用旧版本的 Transformers.js，它在 Worker 中更稳定
+        const { pipeline: pipelineFactory, env, AutoTokenizer } = await import('@xenova/transformers');
+        
+        // 配置环境以适应浏览器 Worker
+        env.allowLocalModels = false;
+        env.allowRemoteModels = true;
+        
+        // 配置 WASM 后端
+        env.backends.onnx.wasm.numThreads = 2; // 在 Worker 中使用单线程
+        env.backends.onnx.wasm.simd = true;
+        
+        // 禁用 Node.js 特定功能
+        env.useFS = false;
+        env.useBrowserCache = true;
+        
+        // 存储导入的函数
+        (globalThis as any).pipelineFactory = pipelineFactory;
+        (globalThis as any).AutoTokenizer = AutoTokenizer;
+        (globalThis as any).env = env;
+        
+        transformersLoaded = true;
+        console.log('Transformers.js loaded successfully');
+    } catch (error) {
+        console.error('Failed to load Transformers.js:', error);
+        throw new Error(`Failed to load Transformers.js: ${error}`);
+    }
+}
+
+// 加载模型
+async function loadModel(modelKey: string, useGpu: boolean = false) {
+    try {
+        console.log(`Loading model: ${modelKey}, GPU: ${useGpu}`);
+        
+        // 确保 Transformers.js 已加载
+        await loadTransformers();
+        
+        const pipelineFactory = (globalThis as any).pipelineFactory;
+        const AutoTokenizer = (globalThis as any).AutoTokenizer;
+        const env = (globalThis as any).env;
+        
+        // 配置管道选项
+        const pipelineOpts: any = {
+            quantized: true,
+            progress_callback: (progress: any) => {
+                console.log('Model loading progress:', progress);
+            }
+        };
+        
+        if (useGpu && typeof navigator !== 'undefined' && 'gpu' in navigator) {
+            console.log('[Transformers] Attempting to use GPU');
+            try {
+                pipelineOpts.device = 'webgpu';
+                pipelineOpts.dtype = 'fp32';
+            } catch (error) {
+                console.warn('[Transformers] GPU not available, falling back to CPU');
+            }
+        } else {
+            console.log('[Transformers] Using CPU');
+        }
+        
+        // 创建嵌入管道
+        pipeline = await pipelineFactory('feature-extraction', modelKey, pipelineOpts);
+        
+        // 创建分词器
+        tokenizer = await AutoTokenizer.from_pretrained(modelKey);
+        
+        model = {
+            loaded: true,
+            model_key: modelKey,
+            use_gpu: useGpu
+        };
+        
+        console.log(`Model ${modelKey} loaded successfully`);
+        return { model_loaded: true };
+        
+    } catch (error) {
+        console.error('Error loading model:', error);
+        throw new Error(`Failed to load model: ${error}`);
+    }
+}
+
+// 卸载模型
+async function unloadModel() {
+    try {
+        console.log('Unloading model...');
+        
+        if (pipeline) {
+            if (pipeline.destroy) {
+                pipeline.destroy();
+            }
+            pipeline = null;
+        }
+        
+        if (tokenizer) {
+            tokenizer = null;
+        }
+        
+        model = null;
+        
+        console.log('Model unloaded successfully');
+        return { model_unloaded: true };
+        
+    } catch (error) {
+        console.error('Error unloading model:', error);
+        throw new Error(`Failed to unload model: ${error}`);
+    }
+}
+
+// 计算 token 数量
+async function countTokens(input: string) {
+    try {
+        if (!tokenizer) {
+            throw new Error('Tokenizer not loaded');
+        }
+        
+        const { input_ids } = await tokenizer(input);
+        return { tokens: input_ids.data.length };
+        
+    } catch (error) {
+        console.error('Error counting tokens:', error);
+        throw new Error(`Failed to count tokens: ${error}`);
+    }
+}
+
+// 生成嵌入向量
+async function embedBatch(inputs: EmbedInput[]): Promise<EmbedResult[]> {
+    try {
+        if (!pipeline || !tokenizer) {
+            throw new Error('Model not loaded');
+        }
+        
+        console.log(`Processing ${inputs.length} inputs`);
+        
+        // 过滤空输入
+        const filteredInputs = inputs.filter(item => item.embed_input && item.embed_input.length > 0);
+        
+        if (filteredInputs.length === 0) {
+            return [];
+        }
+        
+        // 批处理大小（可以根据需要调整）
+        const batchSize = 1;
+        
+        if (filteredInputs.length > batchSize) {
+            console.log(`Processing ${filteredInputs.length} inputs in batches of ${batchSize}`);
+            const results: EmbedResult[] = [];
+            
+            for (let i = 0; i < filteredInputs.length; i += batchSize) {
+                const batch = filteredInputs.slice(i, i + batchSize);
+                const batchResults = await processBatch(batch);
+                results.push(...batchResults);
+            }
+            
+            return results;
+        }
+        
+        return await processBatch(filteredInputs);
+        
+    } catch (error) {
+        console.error('Error in embed batch:', error);
+        throw new Error(`Failed to generate embeddings: ${error}`);
+    }
+}
+
+// 处理单个批次
+async function processBatch(batchInputs: EmbedInput[]): Promise<EmbedResult[]> {
+    try {
+        // 计算每个输入的 token 数量
+        const tokens = await Promise.all(
+            batchInputs.map(item => countTokens(item.embed_input))
+        );
+        
+        // 准备嵌入输入（处理超长文本）
+        const maxTokens = 512; // 大多数模型的最大 token 限制
+        const embedInputs = await Promise.all(
+            batchInputs.map(async (item, i) => {
+                if (tokens[i].tokens < maxTokens) {
+                    return item.embed_input;
+                }
+                
+                // 截断超长文本
+                let tokenCt = tokens[i].tokens;
+                let truncatedInput = item.embed_input;
+                
+                while (tokenCt > maxTokens) {
+                    const pct = maxTokens / tokenCt;
+                    const maxChars = Math.floor(truncatedInput.length * pct * 0.9);
+                    truncatedInput = truncatedInput.substring(0, maxChars) + '...';
+                    tokenCt = (await countTokens(truncatedInput)).tokens;
+                }
+                
+                tokens[i].tokens = tokenCt;
+                return truncatedInput;
+            })
+        );
+        
+        // 生成嵌入向量
+        const resp = await pipeline(embedInputs, { pooling: 'mean', normalize: true });
+        
+        // 处理结果
+        return batchInputs.map((item, i) => ({
+            vec: Array.from(resp[i].data).map((val: number) => Math.round(val * 1e8) / 1e8),
+            tokens: tokens[i].tokens,
+            embed_input: item.embed_input
+        }));
+        
+    } catch (error) {
+        console.error('Error processing batch:', error);
+        
+        // 如果批处理失败，尝试逐个处理
+        return Promise.all(
+            batchInputs.map(async (item) => {
+                try {
+                    const result = await pipeline(item.embed_input, { pooling: 'mean', normalize: true });
+                    const tokenCount = await countTokens(item.embed_input);
+                    
+                    return {
+                        vec: Array.from(result[0].data).map((val: number) => Math.round(val * 1e8) / 1e8),
+                        tokens: tokenCount.tokens,
+                        embed_input: item.embed_input
+                    };
+                } catch (singleError) {
+                    console.error('Error processing single item:', singleError);
+                    return {
+                        vec: [],
+                        tokens: 0,
+                        embed_input: item.embed_input,
+                        error: (singleError as Error).message
+                    } as any;
+                }
+            })
+        );
+    }
+}
+
+// 处理消息
+async function processMessage(data: WorkerMessage): Promise<WorkerResponse> {
+    const { method, params, id, worker_id } = data;
+    
+    try {
+        let result: any;
+        
+        switch (method) {
+            case 'load':
+                console.log('Load method called with params:', params);
+                result = await loadModel(params.model_key, params.use_gpu || false);
+                break;
+                
+            case 'unload':
+                console.log('Unload method called');
+                result = await unloadModel();
+                break;
+                
+            case 'embed_batch':
+                console.log('Embed batch method called');
+                if (!model) {
+                    throw new Error('Model not loaded');
+                }
+                
+                // 等待之前的处理完成
+                if (processing_message) {
+                    while (processing_message) {
+                        await new Promise(resolve => setTimeout(resolve, 100));
+                    }
+                }
+                
+                processing_message = true;
+                result = await embedBatch(params.inputs);
+                processing_message = false;
+                break;
+                
+            case 'count_tokens':
+                console.log('Count tokens method called');
+                if (!model) {
+                    throw new Error('Model not loaded');
+                }
+                
+                // 等待之前的处理完成
+                if (processing_message) {
+                    while (processing_message) {
+                        await new Promise(resolve => setTimeout(resolve, 100));
+                    }
+                }
+                
+                processing_message = true;
+                result = await countTokens(params);
+                processing_message = false;
+                break;
+                
+            default:
+                throw new Error(`Unknown method: ${method}`);
+        }
+        
+        return { id, result, worker_id };
+        
+    } catch (error) {
+        console.error('Error processing message:', error);
+        processing_message = false;
+        return { id, error: (error as Error).message, worker_id };
+    }
+}
+
+// 监听消息
+self.addEventListener('message', async (event) => {
+    console.log('Worker received message:', event.data);
+    const response = await processMessage(event.data);
+    console.log('Worker sending response:', response);
+    self.postMessage(response);
+});
+
+console.log('Embedding worker ready'); 

src/embedworker/index.ts

@@ -0,0 +1,15 @@
+import { EmbeddingManager } from "./EmbeddingManager";
+
+// 创建一个单例的 Manager，以便在整个应用中共享同一个 Worker
+export const embeddingManager = new EmbeddingManager();
+
+// 导出 EmbeddingManager 类以便其他地方使用
+export { EmbeddingManager };
+
+// 导出类型定义
+export type {
+    EmbedResult,
+    ModelLoadResult,
+    ModelUnloadResult,
+    TokenCountResult
+} from './EmbeddingManager';

src/hooks/use-chat-history.ts

@@ -4,8 +4,10 @@ import { useCallback, useEffect, useMemo, useState } from 'react'
 
 import { editorStateToPlainText } from '../components/chat-view/chat-input/utils/editor-state-to-plain-text'
 import { useApp } from '../contexts/AppContext'
+import { useSettings } from '../contexts/SettingsContext'
 import { ChatManager } from '../database/json/chat/ChatManager'
 import { deserializeChatMessage, serializeChatMessage } from '../database/json/utils'
+import { WorkspaceManager } from '../database/json/workspace/WorkspaceManager'
 import { ChatConversationMeta, ChatMessage, ChatUserMessage } from '../types/chat'
 
 type UseChatHistory = {
@@ -22,7 +24,9 @@ type UseChatHistory = {
 
 export function useChatHistory(): UseChatHistory {
 	const app = useApp()
-	const chatManager = useMemo(() => new ChatManager(app), [app])
+	const { settings } = useSettings()
+	const workspaceManager = useMemo(() => new WorkspaceManager(app), [app])
+	const chatManager = useMemo(() => new ChatManager(app, workspaceManager), [app, workspaceManager])
 
 	const [chatList, setChatList] = useState<ChatConversationMeta[]>([])
 
@@ -31,6 +35,9 @@ export function useChatHistory(): UseChatHistory {
 		setChatList(conversations)
 	}, [chatManager])
 
+	// 获取当前工作区
+	const currentWorkspace = settings.workspace || 'vault'
+
 	useEffect(() => {
 		void fetchChatList()
 	}, [fetchChatList])
@@ -52,7 +59,7 @@ export function useChatHistory(): UseChatHistory {
 					} else {
 						const firstUserMessage = messages.find((v) => v.role === 'user') as ChatUserMessage
 
-						const newChat = await chatManager.createChat({
+						await chatManager.createChat({
 							id,
 							title: firstUserMessage?.content
 								? editorStateToPlainText(firstUserMessage.content).substring(
@@ -61,6 +68,7 @@ export function useChatHistory(): UseChatHistory {
 								)
 								: 'New chat',
 							messages: serializedMessages,
+							workspace: currentWorkspace,
 						})
 					}
 
@@ -71,7 +79,7 @@ export function useChatHistory(): UseChatHistory {
 					maxWait: 1000,
 				},
 			),
-		[chatManager, fetchChatList],
+		[chatManager, fetchChatList, settings, workspaceManager],
 	)
 
 	const deleteConversation = useCallback(

src/main.ts

@@ -1,7 +1,7 @@
 // @ts-nocheck
 import { EditorView } from '@codemirror/view'
 // import { PGlite } from '@electric-sql/pglite'
-import { App, Editor, MarkdownView, Modal, Notice, Plugin, TFile } from 'obsidian'
+import { Editor, MarkdownView, Modal, Notice, Plugin, TFile } from 'obsidian'
 
 import { ApplyView } from './ApplyView'
 import { ChatView } from './ChatView'
@@ -419,6 +419,47 @@ export default class InfioPlugin extends Plugin {
 				}
 			},
 		});
+
+		// 添加本地嵌入测试命令
+		this.addCommand({
+			id: 'test-local-embed',
+			name: '测试本地嵌入模型',
+			callback: async () => {
+				try {
+					// 动态导入嵌入管理器
+					const { embeddingManager } = await import('./embedworker/index');
+										
+					// 加载模型
+					await embeddingManager.loadModel("Xenova/all-MiniLM-L6-v2", true);
+					
+					// 测试嵌入 "hello world"
+					const testText = "hello world";
+					
+					const result = await embeddingManager.embed(testText);
+					
+					// 显示结果
+					const resultMessage = `
+	嵌入测试完成！
+	文本: "${testText}"
+	Token 数量: ${result.tokens}
+	向量维度: ${result.vec.length}
+	向量前4个值: [${result.vec.slice(0, 4).map(v => v.toFixed(4)).join(', ')}...]
+					`.trim();
+					
+					console.log('本地嵌入测试结果:', result);
+					
+					// 创建模态框显示结果
+					const modal = new Modal(this.app);
+					modal.titleEl.setText('本地嵌入测试结果');
+					modal.contentEl.createEl('pre', { text: resultMessage });
+					modal.open();
+					
+				} catch (error) {
+					console.error('嵌入测试失败:', error);
+					new Notice(`嵌入测试失败: ${error.message}`, 5000);
+				}
+			},
+		});
 	}
 
 	onunload() {

src/types/chat.ts

@@ -81,4 +81,5 @@ export type ChatConversationMeta = {
 	title: string
 	createdAt: number
 	updatedAt: number
+	workspace?: string
 }

src/utils/parse-infio-block.ts

@@ -1,4 +1,4 @@
-// @ts-expect-error - parse5 and JSON5 types are not perfectly aligned with the dynamic parsing logic
+// @ts-nocheck
 import JSON5 from 'json5'
 import { parseFragment } from 'parse5'
 

tsconfig.json

@@ -71,5 +71,5 @@
     // }
   },
   // Specify which files to include in compilation
-  "include": ["src/**/*.ts", "src/**/*.tsx", "__mocks__"]
+  	"include": ["src/**/*.ts", "src/**/*.tsx", "__mocks__", "src/embedworker/*.worker.ts", "src/embedworker/*.worker.js"]
 }