Moved embeddings to worker to prevent blocking

src/embedder.ts

@@ -0,0 +1,11 @@
+import { pipeline } from '@xenova/transformers';
+import { parentPort } from 'worker_threads';
+
+let model: any;
+
+parentPort?.on('message', async ({ id, text }) => {
+	if(!model) model = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
+	const output = await model(text, { pooling: 'mean', normalize: true });
+	const embedding = Array.from(output.data);
+	parentPort?.postMessage({ id, embedding });
+});

src/llm.ts

@@ -1,4 +1,3 @@
-import {pipeline} from '@xenova/transformers';
 import {JSONAttemptParse} from '@ztimson/utils';
 import {Ai} from './ai.ts';
 import {Anthropic} from './antrhopic.ts';
@@ -6,7 +5,9 @@ import {Ollama} from './ollama.ts';
 import {OpenAi} from './open-ai.ts';
 import {AbortablePromise, LLMProvider} from './provider.ts';
 import {AiTool} from './tools.ts';
-import * as tf from '@tensorflow/tfjs';
+import {Worker} from 'worker_threads';
+import {fileURLToPath} from 'url';
+import {dirname, join} from 'path';
 
 export type LLMMessage = {
 	/** Message originator */
@@ -83,11 +84,22 @@ export type LLMRequest = {
 }
 
 export class LLM {
-	private embedModel: any;
+	private embedWorker: Worker | null = null;
+	private embedQueue = new Map<number, { resolve: (value: number[]) => void; reject: (error: any) => void }>();
+	private embedId = 0;
 	private providers: {[key: string]: LLMProvider} = {};
 
+
 	constructor(public readonly ai: Ai) {
-		this.embedModel = pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
+		this.embedWorker = new Worker(join(dirname(fileURLToPath(import.meta.url)), 'embedder.js'));
+		this.embedWorker.on('message', ({ id, embedding }) => {
+			const pending = this.embedQueue.get(id);
+			if (pending) {
+				pending.resolve(embedding);
+				this.embedQueue.delete(id);
+			}
+		});
+
 		if(ai.options.anthropic?.token) this.providers.anthropic = new Anthropic(this.ai, ai.options.anthropic.token, ai.options.anthropic.model);
 		if(ai.options.ollama?.host) this.providers.ollama = new Ollama(this.ai, ai.options.ollama.host, ai.options.ollama.model);
 		if(ai.options.openAi?.token) this.providers.openAi = new OpenAi(this.ai, ai.options.openAi.token, ai.options.openAi.model);
@@ -159,10 +171,12 @@ export class LLM {
 			});
 		};
 
-		const embed = async (text: string): Promise<number[]> => {
-			const model = await this.embedModel;
-			const output = await model(text, {pooling: 'mean', normalize: true});
-			return Array.from(output.data);
+		const embed = (text: string): Promise<number[]> => {
+			return new Promise((resolve, reject) => {
+				const id = this.embedId++;
+				this.embedQueue.set(id, { resolve, reject });
+				this.embedWorker?.postMessage({ id, text });
+			});
 		};
 
 		// Tokenize
@@ -188,7 +202,7 @@ export class LLM {
 			const cleanText = text.replace(/\s*\n\s*/g, '\n').trim();
 			if(cleanText) chunks.push(cleanText);
 			start = end - overlapTokens;
-			if (start <= end - tokens.length + end) start = end; // Safety: prevent infinite loop
+			if(start <= end - tokens.length + end) start = end;
 		}
 
 		return Promise.all(chunks.map(async (text, index) => ({