mirror of
https://github.com/EthanMarti/infio-copilot.git
synced 2026-01-16 08:21:55 +00:00
更新 RAGEngine 和嵌入管理器以支持嵌入管理器的传递，添加本地提供者的嵌入模型加载逻辑，优化错误处理和消息处理机制。
This commit is contained in:
duanfuxiang
parent
bed96a5233
commit
4e139ecc4f
@ -16,10 +16,67 @@ import {
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} from '../llm/exception'
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import { NoStainlessOpenAI } from '../llm/ollama'
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// EmbeddingManager 类型定义
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type EmbeddingManager = {
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modelLoaded: boolean
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currentModel: string | null
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loadModel(modelId: string, useGpu: boolean): Promise<any>
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embed(text: string): Promise<{ vec: number[] }>
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embedBatch(texts: string[]): Promise<{ vec: number[] }[]>
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}
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export const getEmbeddingModel = (
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settings: InfioSettings,
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embeddingManager?: EmbeddingManager,
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): EmbeddingModel => {
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switch (settings.embeddingModelProvider) {
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case ApiProvider.LocalProvider: {
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if (!embeddingManager) {
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throw new Error('EmbeddingManager is required for LocalProvider')
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}
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const modelInfo = GetEmbeddingModelInfo(settings.embeddingModelProvider, settings.embeddingModelId)
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if (!modelInfo) {
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throw new Error(`Embedding model ${settings.embeddingModelId} not found for provider ${settings.embeddingModelProvider}`)
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}
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return {
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id: settings.embeddingModelId,
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dimension: modelInfo.dimensions,
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supportsBatch: true,
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getEmbedding: async (text: string) => {
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try {
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// 确保模型已加载
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if (!embeddingManager.modelLoaded || embeddingManager.currentModel !== settings.embeddingModelId) {
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console.log(`Loading model: ${settings.embeddingModelId}`)
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await embeddingManager.loadModel(settings.embeddingModelId, true)
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}
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const result = await embeddingManager.embed(text)
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return result.vec
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} catch (error) {
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console.error('LocalProvider embedding error:', error)
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throw new Error(`LocalProvider embedding failed: ${error.message}`)
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}
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},
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getBatchEmbeddings: async (texts: string[]) => {
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try {
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// 确保模型已加载
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if (!embeddingManager.modelLoaded || embeddingManager.currentModel !== settings.embeddingModelId) {
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console.log(`Loading model: ${settings.embeddingModelId}`)
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await embeddingManager.loadModel(settings.embeddingModelId, false)
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}
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const results = await embeddingManager.embedBatch(texts)
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console.log('results', results)
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return results.map(result => result.vec)
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} catch (error) {
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console.error('LocalProvider batch embedding error:', error)
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throw new Error(`LocalProvider batch embedding failed: ${error.message}`)
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}
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},
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}
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}
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case ApiProvider.Infio: {
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const openai = new OpenAI({
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apiKey: settings.infioProvider.apiKey,
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@ -10,9 +10,19 @@ import { InfioSettings } from '../../types/settings'
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import { getEmbeddingModel } from './embedding'
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// EmbeddingManager 类型定义
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type EmbeddingManager = {
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modelLoaded: boolean
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currentModel: string | null
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loadModel(modelId: string, useGpu: boolean): Promise<any>
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embed(text: string): Promise<{ vec: number[] }>
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embedBatch(texts: string[]): Promise<{ vec: number[] }[]>
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}
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export class RAGEngine {
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private app: App
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private settings: InfioSettings
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private embeddingManager?: EmbeddingManager
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private vectorManager: VectorManager | null = null
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private embeddingModel: EmbeddingModel | null = null
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private initialized = false
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@ -21,13 +31,15 @@ export class RAGEngine {
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app: App,
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settings: InfioSettings,
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dbManager: DBManager,
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embeddingManager?: EmbeddingManager,
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) {
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this.app = app
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this.settings = settings
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this.embeddingManager = embeddingManager
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this.vectorManager = dbManager.getVectorManager()
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if (settings.embeddingModelId && settings.embeddingModelId.trim() !== '') {
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try {
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this.embeddingModel = getEmbeddingModel(settings)
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this.embeddingModel = getEmbeddingModel(settings, embeddingManager)
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} catch (error) {
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console.warn('Failed to initialize embedding model:', error)
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this.embeddingModel = null
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@ -46,7 +58,7 @@ export class RAGEngine {
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this.settings = settings
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if (settings.embeddingModelId && settings.embeddingModelId.trim() !== '') {
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try {
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this.embeddingModel = getEmbeddingModel(settings)
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this.embeddingModel = getEmbeddingModel(settings, this.embeddingManager)
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} catch (error) {
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console.warn('Failed to initialize embedding model:', error)
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this.embeddingModel = null
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@ -113,7 +113,7 @@ export class VectorManager {
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const textSplitter = new MarkdownTextSplitter({
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chunkSize: options.chunkSize,
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chunkOverlap: Math.floor(options.chunkSize * 0.15)
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// chunkOverlap: Math.floor(options.chunkSize * 0.15)
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})
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const skippedFiles: string[] = []
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@ -34,19 +34,28 @@ export class EmbeddingManager {
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// 统一监听来自 Worker 的所有消息
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this.worker.onmessage = (event) => {
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const { id, result, error } = event.data;
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try {
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const { id, result, error } = event.data;
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// 根据返回的 id 找到对应的 Promise 回调
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const request = this.requests.get(id);
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// 根据返回的 id 找到对应的 Promise 回调
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const request = this.requests.get(id);
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if (request) {
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if (error) {
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request.reject(new Error(error));
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} else {
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request.resolve(result);
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if (request) {
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if (error) {
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request.reject(new Error(error));
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} else {
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request.resolve(result);
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}
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// 完成后从 Map 中删除
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this.requests.delete(id);
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}
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// 完成后从 Map 中删除
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this.requests.delete(id);
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} catch (err) {
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console.error("Error processing worker message:", err);
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// 拒绝所有待处理的请求
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this.requests.forEach(request => {
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request.reject(new Error(`Worker message processing error: ${err.message}`));
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});
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this.requests.clear();
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}
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};
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@ -54,9 +63,13 @@ export class EmbeddingManager {
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console.error("EmbeddingWorker error:", error);
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// 拒绝所有待处理的请求
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this.requests.forEach(request => {
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request.reject(error);
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request.reject(new Error(`Worker error: ${error.message || 'Unknown worker error'}`));
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});
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this.requests.clear();
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// 重置状态
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this.isModelLoaded = false;
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this.currentModelId = null;
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};
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}
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@ -3,27 +3,27 @@ console.log('Embedding worker loaded');
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// 类型定义
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interface EmbedInput {
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embed_input: string;
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embed_input: string;
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}
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interface EmbedResult {
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vec: number[];
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tokens: number;
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embed_input?: string;
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vec: number[];
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tokens: number;
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embed_input?: string;
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}
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interface WorkerMessage {
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method: string;
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params: any;
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id: number;
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worker_id?: string;
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method: string;
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params: any;
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id: number;
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worker_id?: string;
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}
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interface WorkerResponse {
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id: number;
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result?: any;
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error?: string;
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worker_id?: string;
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id: number;
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result?: any;
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error?: string;
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worker_id?: string;
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}
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// 全局变量
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@ -35,319 +35,377 @@ let transformersLoaded = false;
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// 动态导入 Transformers.js
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async function loadTransformers() {
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if (transformersLoaded) return;
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try {
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console.log('Loading Transformers.js...');
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// 尝试使用旧版本的 Transformers.js，它在 Worker 中更稳定
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const { pipeline: pipelineFactory, env, AutoTokenizer } = await import('@xenova/transformers');
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// 配置环境以适应浏览器 Worker
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env.allowLocalModels = false;
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env.allowRemoteModels = true;
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// 配置 WASM 后端
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env.backends.onnx.wasm.numThreads = 2; // 在 Worker 中使用单线程
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env.backends.onnx.wasm.simd = true;
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// 禁用 Node.js 特定功能
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env.useFS = false;
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env.useBrowserCache = true;
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// 存储导入的函数
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(globalThis as any).pipelineFactory = pipelineFactory;
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(globalThis as any).AutoTokenizer = AutoTokenizer;
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(globalThis as any).env = env;
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transformersLoaded = true;
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console.log('Transformers.js loaded successfully');
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} catch (error) {
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console.error('Failed to load Transformers.js:', error);
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throw new Error(`Failed to load Transformers.js: ${error}`);
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}
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if (transformersLoaded) return;
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try {
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console.log('Loading Transformers.js...');
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// 尝试使用旧版本的 Transformers.js，它在 Worker 中更稳定
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const { pipeline: pipelineFactory, env, AutoTokenizer } = await import('@xenova/transformers');
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// 配置环境以适应浏览器 Worker
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env.allowLocalModels = false;
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env.allowRemoteModels = true;
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// 配置 WASM 后端 - 修复线程配置
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env.backends.onnx.wasm.numThreads = 4; // 在 Worker 中使用单线程，避免竞态条件
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env.backends.onnx.wasm.simd = true;
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// 禁用 Node.js 特定功能
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env.useFS = false;
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env.useBrowserCache = true;
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// 存储导入的函数
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(globalThis as any).pipelineFactory = pipelineFactory;
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(globalThis as any).AutoTokenizer = AutoTokenizer;
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(globalThis as any).env = env;
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transformersLoaded = true;
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console.log('Transformers.js loaded successfully');
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} catch (error) {
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console.error('Failed to load Transformers.js:', error);
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throw new Error(`Failed to load Transformers.js: ${error}`);
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}
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}
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// 加载模型
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async function loadModel(modelKey: string, useGpu: boolean = false) {
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try {
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console.log(`Loading model: ${modelKey}, GPU: ${useGpu}`);
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// 确保 Transformers.js 已加载
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await loadTransformers();
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const pipelineFactory = (globalThis as any).pipelineFactory;
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const AutoTokenizer = (globalThis as any).AutoTokenizer;
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const env = (globalThis as any).env;
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// 配置管道选项
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const pipelineOpts: any = {
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quantized: true,
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progress_callback: (progress: any) => {
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console.log('Model loading progress:', progress);
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}
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};
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if (useGpu && typeof navigator !== 'undefined' && 'gpu' in navigator) {
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console.log('[Transformers] Attempting to use GPU');
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try {
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pipelineOpts.device = 'webgpu';
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pipelineOpts.dtype = 'fp32';
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} catch (error) {
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console.warn('[Transformers] GPU not available, falling back to CPU');
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}
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} else {
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console.log('[Transformers] Using CPU');
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}
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// 创建嵌入管道
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pipeline = await pipelineFactory('feature-extraction', modelKey, pipelineOpts);
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// 创建分词器
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tokenizer = await AutoTokenizer.from_pretrained(modelKey);
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model = {
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loaded: true,
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model_key: modelKey,
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use_gpu: useGpu
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};
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console.log(`Model ${modelKey} loaded successfully`);
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return { model_loaded: true };
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} catch (error) {
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console.error('Error loading model:', error);
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throw new Error(`Failed to load model: ${error}`);
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}
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try {
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console.log(`Loading model: ${modelKey}, GPU: ${useGpu}`);
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// 确保 Transformers.js 已加载
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await loadTransformers();
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const pipelineFactory = (globalThis as any).pipelineFactory;
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const AutoTokenizer = (globalThis as any).AutoTokenizer;
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const env = (globalThis as any).env;
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// 配置管道选项
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const pipelineOpts: any = {
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quantized: true,
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// 修复进度回调，添加错误处理
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progress_callback: (progress: any) => {
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try {
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if (progress && typeof progress === 'object') {
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console.log('Model loading progress:', progress);
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}
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} catch (error) {
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// 忽略进度回调错误，避免中断模型加载
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console.warn('Progress callback error (ignored):', error);
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}
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}
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};
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// GPU 配置更加谨慎
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if (useGpu) {
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try {
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// 检查 WebGPU 支持
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console.log("useGpu", useGpu)
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if (typeof navigator !== 'undefined' && 'gpu' in navigator) {
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const gpu = (navigator as any).gpu;
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if (gpu && typeof gpu.requestAdapter === 'function') {
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console.log('[Transformers] Attempting to use GPU');
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pipelineOpts.device = 'webgpu';
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pipelineOpts.dtype = 'fp32';
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} else {
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console.log('[Transformers] WebGPU not fully supported, using CPU');
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}
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} else {
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console.log('[Transformers] WebGPU not available, using CPU');
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}
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} catch (error) {
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console.warn('[Transformers] Error checking GPU support, falling back to CPU:', error);
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}
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} else {
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console.log('[Transformers] Using CPU');
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}
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// 创建嵌入管道
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pipeline = await pipelineFactory('feature-extraction', modelKey, pipelineOpts);
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// 创建分词器
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tokenizer = await AutoTokenizer.from_pretrained(modelKey);
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model = {
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loaded: true,
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model_key: modelKey,
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use_gpu: useGpu
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};
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console.log(`Model ${modelKey} loaded successfully`);
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return { model_loaded: true };
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} catch (error) {
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console.error('Error loading model:', error);
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throw new Error(`Failed to load model: ${error}`);
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}
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}
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// 卸载模型
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async function unloadModel() {
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try {
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console.log('Unloading model...');
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if (pipeline) {
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if (pipeline.destroy) {
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pipeline.destroy();
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}
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pipeline = null;
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}
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if (tokenizer) {
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tokenizer = null;
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}
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model = null;
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console.log('Model unloaded successfully');
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return { model_unloaded: true };
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} catch (error) {
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console.error('Error unloading model:', error);
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throw new Error(`Failed to unload model: ${error}`);
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}
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try {
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console.log('Unloading model...');
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if (pipeline) {
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if (pipeline.destroy) {
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pipeline.destroy();
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}
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pipeline = null;
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}
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if (tokenizer) {
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tokenizer = null;
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}
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model = null;
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console.log('Model unloaded successfully');
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return { model_unloaded: true };
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} catch (error) {
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console.error('Error unloading model:', error);
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throw new Error(`Failed to unload model: ${error}`);
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}
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}
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// 计算 token 数量
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async function countTokens(input: string) {
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try {
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if (!tokenizer) {
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throw new Error('Tokenizer not loaded');
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}
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const { input_ids } = await tokenizer(input);
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return { tokens: input_ids.data.length };
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} catch (error) {
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console.error('Error counting tokens:', error);
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throw new Error(`Failed to count tokens: ${error}`);
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}
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try {
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if (!tokenizer) {
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throw new Error('Tokenizer not loaded');
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}
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const { input_ids } = await tokenizer(input);
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return { tokens: input_ids.data.length };
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} catch (error) {
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console.error('Error counting tokens:', error);
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throw new Error(`Failed to count tokens: ${error}`);
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}
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}
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// 生成嵌入向量
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async function embedBatch(inputs: EmbedInput[]): Promise<EmbedResult[]> {
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try {
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if (!pipeline || !tokenizer) {
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throw new Error('Model not loaded');
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}
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|
||||
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}`);
|
||||
}
|
||||
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;
|
||||
}
|
||||
})
|
||||
);
|
||||
}
|
||||
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 };
|
||||
}
|
||||
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);
|
||||
try {
|
||||
console.log('Worker received message:', event.data);
|
||||
|
||||
// 验证消息格式
|
||||
if (!event.data || typeof event.data !== 'object') {
|
||||
console.error('Invalid message format received');
|
||||
self.postMessage({
|
||||
id: -1,
|
||||
error: 'Invalid message format'
|
||||
});
|
||||
return;
|
||||
}
|
||||
|
||||
const response = await processMessage(event.data);
|
||||
console.log('Worker sending response:', response);
|
||||
self.postMessage(response);
|
||||
} catch (error) {
|
||||
console.error('Unhandled error in worker message handler:', error);
|
||||
self.postMessage({
|
||||
id: event.data?.id || -1,
|
||||
error: `Worker error: ${error.message || 'Unknown error'}`
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
// 添加全局错误处理
|
||||
self.addEventListener('error', (event) => {
|
||||
console.error('Worker global error:', event);
|
||||
self.postMessage({
|
||||
id: -1,
|
||||
error: `Worker global error: ${event.message || 'Unknown error'}`
|
||||
});
|
||||
});
|
||||
|
||||
// 添加未处理的 Promise 拒绝处理
|
||||
self.addEventListener('unhandledrejection', (event) => {
|
||||
console.error('Worker unhandled promise rejection:', event);
|
||||
self.postMessage({
|
||||
id: -1,
|
||||
error: `Worker unhandled rejection: ${event.reason || 'Unknown error'}`
|
||||
});
|
||||
event.preventDefault(); // 防止默认的控制台错误
|
||||
});
|
||||
|
||||
console.log('Embedding worker ready');
|
||||
|
||||
@ -623,7 +623,7 @@ export default class InfioPlugin extends Plugin {
|
||||
if (!this.ragEngineInitPromise) {
|
||||
this.ragEngineInitPromise = (async () => {
|
||||
const dbManager = await this.getDbManager()
|
||||
this.ragEngine = new RAGEngine(this.app, this.settings, dbManager)
|
||||
this.ragEngine = new RAGEngine(this.app, this.settings, dbManager, this.embeddingManager)
|
||||
return this.ragEngine
|
||||
})()
|
||||
}
|
||||
|
||||
@ -14,6 +14,7 @@ export const providerApiUrls: Record<ApiProvider, string> = {
|
||||
[ApiProvider.Grok]: 'https://console.x.ai/',
|
||||
[ApiProvider.Ollama]: '', // Ollama 不需要API Key
|
||||
[ApiProvider.OpenAICompatible]: '', // 自定义兼容API，无固定URL
|
||||
[ApiProvider.LocalProvider]: '', // 本地提供者，无固定URL
|
||||
};
|
||||
|
||||
// 获取指定provider的API Key获取URL
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user