Pulled chunking out into its own exported function for easy access

package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "0.2.3",
+	"version": "0.2.7",
 	"description": "AI Utility library",
 	"author": "Zak Timson",
 	"license": "MIT",

src/ai.ts

@@ -1,22 +1,26 @@
+import * as os from 'node:os';
 import {LLM, LLMOptions} from './llm';
 import { Audio } from './audio.ts';
 import {Vision} from './vision.ts';
 
 export type AiOptions = LLMOptions & {
+	/** Path to models */
+	path?: string;
+	/** Whisper ASR configuration */
 	whisper?: {
 		/** Whisper binary location */
 		binary: string;
 		/** Model: `ggml-base.en.bin` */
 		model: string;
 	}
-	/** Path to models */
+	/** Tesseract OCR configuration */
-	path: string;
+	tesseract?: {
+		/** Model: eng, eng_best, eng_fast */
+		model?: string;
+	}
 }
 
 export class Ai {
-	private downloads: {[key: string]: Promise<string>} = {};
-	private whisperModel!: string;
-
 	/** Audio processing AI */
 	audio!: Audio;
 	/** Language processing AI */
@@ -25,6 +29,7 @@ export class Ai {
 	vision!: Vision;
 
 	constructor(public readonly options: AiOptions) {
+		if(!options.path) options.path = os.tmpdir();
 		process.env.TRANSFORMERS_CACHE = options.path;
 		this.audio = new Audio(this);
 		this.language = new LLM(this);

src/antrhopic.ts

@@ -54,12 +54,14 @@ export class Anthropic extends LLMProvider {
 			let history = this.fromStandard([...options.history || [], {role: 'user', content: message, timestamp: Date.now()}]);
 			const original = deepCopy(history);
 			if(options.compress) history = await this.ai.language.compressHistory(<any>history, options.compress.max, options.compress.min, options);
+
+			const tools = options.tools || this.ai.options.tools || [];
 			const requestParams: any = {
 				model: options.model || this.model,
 				max_tokens: options.max_tokens || this.ai.options.max_tokens || 4096,
 				system: options.system || this.ai.options.system || '',
 				temperature: options.temperature || this.ai.options.temperature || 0.7,
-				tools: (options.tools || this.ai.options.tools || []).map(t => ({
+				tools: tools.map(t => ({
 					name: t.name,
 					description: t.description,
 					input_schema: {
@@ -76,7 +78,10 @@ export class Anthropic extends LLMProvider {
 			let resp: any, isFirstMessage = true;
 			const assistantMessages: string[] = [];
 			do {
-				resp = await this.client.messages.create(requestParams);
+				resp = await this.client.messages.create(requestParams).catch(err => {
+					err.message += `\n\nMessages:\n${JSON.stringify(history, null, 2)}`;
+					throw err;
+				});
 
 				// Streaming mode
 				if(options.stream) {
@@ -114,7 +119,7 @@ export class Anthropic extends LLMProvider {
 					history.push({role: 'assistant', content: resp.content});
 					original.push({role: 'assistant', content: resp.content});
 					const results = await Promise.all(toolCalls.map(async (toolCall: any) => {
-						const tool = options.tools?.find(findByProp('name', toolCall.name));
+						const tool = tools.find(findByProp('name', toolCall.name));
 						if(!tool) return {tool_use_id: toolCall.id, is_error: true, content: 'Tool not found'};
 						try {
 							const result = await tool.fn(toolCall.input, this.ai);

src/audio.ts

@@ -48,7 +48,7 @@ export class Audio {
 	async downloadAsrModel(model: string = this.whisperModel): Promise<string> {
 		if(!this.ai.options.whisper?.binary) throw new Error('Whisper not configured');
 		if(!model.endsWith('.bin')) model += '.bin';
-		const p = Path.join(this.ai.options.path, model);
+		const p = Path.join(<string>this.ai.options.path, model);
 		if(await fs.stat(p).then(() => true).catch(() => false)) return p;
 		if(!!this.downloads[model]) return this.downloads[model];
 		this.downloads[model] = fetch(`https://huggingface.co/ggerganov/whisper.cpp/resolve/main/${model}`)

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);
@@ -148,49 +160,44 @@ export class LLM {
 		return denominator === 0 ? 0 : dotProduct / denominator;
 	}
 
-	embedding(target: object | string, maxTokens = 500, overlapTokens = 50) {
+	chunk(target: object | string, maxTokens = 500, overlapTokens = 50): string[] {
 		const objString = (obj: any, path = ''): string[] => {
-			if(obj === null || obj === undefined) return [];
+			if(!obj) return [];
 			return Object.entries(obj).flatMap(([key, value]) => {
 				const p = path ? `${path}${isNaN(+key) ? `.${key}` : `[${key}]`}` : key;
-				if(typeof value === 'object' && value !== null && !Array.isArray(value)) return objString(value, p);
+				if(typeof value === 'object' && !Array.isArray(value)) return objString(value, p);
-				const valueStr = Array.isArray(value) ? value.join(', ') : String(value);
+				return `${p}: ${Array.isArray(value) ? value.join(', ') : value}`;
-				return `${p}: ${valueStr}`;
 			});
 		};
 
-		const embed = async (text: string): Promise<number[]> => {
+		const lines = typeof target === 'object' ? objString(target) : target.split('\n');
-			const model = await this.embedModel;
+		const tokens = lines.flatMap(l => [...l.split(/\s+/).filter(Boolean), '\n']);
-			const output = await model(text, {pooling: 'mean', normalize: true});
+		const chunks: string[] = [];
-			return Array.from(output.data);
+		for(let i = 0; i < tokens.length;) {
+			let text = '', j = i;
+			while(j < tokens.length) {
+				const next = text + (text ? ' ' : '') + tokens[j];
+				if(this.estimateTokens(next.replace(/\s*\n\s*/g, '\n')) > maxTokens && text) break;
+				text = next;
+				j++;
+			}
+			const clean = text.replace(/\s*\n\s*/g, '\n').trim();
+			if(clean) chunks.push(clean);
+			i = Math.max(j - overlapTokens, j === i ? i + 1 : j);
+		}
+		return chunks;
+	}
+
+	embedding(target: object | string, maxTokens = 500, overlapTokens = 50) {
+		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
+		const chunks = this.chunk(target, maxTokens, overlapTokens);
-		const lines = typeof target === 'object' ? objString(target) : target.split('\n');
-		const tokens = lines.flatMap(line => [...line.split(/\s+/).filter(w => w.trim()), '\n']);
-
-		// Chunk
-		const chunks: string[] = [];
-		let start = 0;
-		while (start < tokens.length) {
-			let end = start;
-			let text = '';
-			// Build chunk
-			while (end < tokens.length) {
-				const nextToken = tokens[end];
-				const testText = text + (text ? ' ' : '') + nextToken;
-				const testTokens = this.estimateTokens(testText.replace(/\s*\n\s*/g, '\n'));
-				if (testTokens > maxTokens && text) break;
-				text = testText;
-				end++;
-			}
-			// Save chunk
-			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
-		}
-
 		return Promise.all(chunks.map(async (text, index) => ({
 			index,
 			embedding: await embed(text),

src/ollama.ts

@@ -49,6 +49,7 @@ export class Ollama extends LLMProvider {
 			if(options.compress) history = await this.ai.language.compressHistory(<any>history, options.compress.max, options.compress.min);
 			if(options.system) history.unshift({role: 'system', content: system})
 
+			const tools = options.tools || this.ai.options.tools || [];
 			const requestParams: any = {
 				model: options.model || this.model,
 				messages: history,
@@ -58,7 +59,7 @@ export class Ollama extends LLMProvider {
 					temperature: options.temperature || this.ai.options.temperature || 0.7,
 					num_predict: options.max_tokens || this.ai.options.max_tokens || 4096,
 				},
-				tools: (options.tools || this.ai.options.tools || []).map(t => ({
+				tools: tools.map(t => ({
 					type: 'function',
 					function: {
 						name: t.name,
@@ -74,7 +75,11 @@ export class Ollama extends LLMProvider {
 
 			let resp: any, isFirstMessage = true;
 			do {
-				resp = await this.client.chat(requestParams);
+				resp = await this.client.chat(requestParams).catch(err => {
+					err.message += `\n\nMessages:\n${JSON.stringify(history, null, 2)}`;
+					throw err;
+				});
+
 				if(options.stream) {
 					if(!isFirstMessage) options.stream({text: '\n\n'});
 					else isFirstMessage = false;
@@ -93,7 +98,7 @@ export class Ollama extends LLMProvider {
 				if(resp.message?.tool_calls?.length && !controller.signal.aborted) {
 					history.push(resp.message);
 					const results = await Promise.all(resp.message.tool_calls.map(async (toolCall: any) => {
-						const tool = (options.tools || this.ai.options.tools)?.find(findByProp('name', toolCall.function.name));
+						const tool = tools.find(findByProp('name', toolCall.function.name));
 						if(!tool) return {role: 'tool', tool_name: toolCall.function.name, content: '{"error": "Tool not found"}'};
 						const args = typeof toolCall.function.arguments === 'string' ? JSONAttemptParse(toolCall.function.arguments, {}) : toolCall.function.arguments;
 						try {

src/open-ai.ts

@@ -67,13 +67,14 @@ export class OpenAi extends LLMProvider {
 			let history = this.fromStandard([...options.history || [], {role: 'user', content: message, timestamp: Date.now()}]);
 			if(options.compress) history = await this.ai.language.compressHistory(<any>history, options.compress.max, options.compress.min, options);
 
+			const tools = options.tools || this.ai.options.tools || [];
 			const requestParams: any = {
 				model: options.model || this.model,
 				messages: history,
 				stream: !!options.stream,
 				max_tokens: options.max_tokens || this.ai.options.max_tokens || 4096,
 				temperature: options.temperature || this.ai.options.temperature || 0.7,
-				tools: (options.tools || this.ai.options.tools || []).map(t => ({
+				tools: tools.map(t => ({
 					type: 'function',
 					function: {
 						name: t.name,
@@ -89,7 +90,11 @@ export class OpenAi extends LLMProvider {
 
 			let resp: any, isFirstMessage = true;
 			do {
-				resp = await this.client.chat.completions.create(requestParams);
+				resp = await this.client.chat.completions.create(requestParams).catch(err => {
+					err.message += `\n\nMessages:\n${JSON.stringify(history, null, 2)}`;
+					throw err;
+				});
+
 				if(options.stream) {
 					if(!isFirstMessage) options.stream({text: '\n\n'});
 					else isFirstMessage = false;
@@ -110,7 +115,7 @@ export class OpenAi extends LLMProvider {
 				if(toolCalls.length && !controller.signal.aborted) {
 					history.push(resp.choices[0].message);
 					const results = await Promise.all(toolCalls.map(async (toolCall: any) => {
-						const tool = options.tools?.find(findByProp('name', toolCall.function.name));
+						const tool = tools?.find(findByProp('name', toolCall.function.name));
 						if(!tool) return {role: 'tool', tool_call_id: toolCall.id, content: '{"error": "Tool not found"}'};
 						try {
 							const args = JSONAttemptParse(toolCall.function.arguments, {});

src/vision.ts

@@ -15,7 +15,7 @@ export class Vision {
 		return {
 			abort: () => { worker?.terminate(); },
 			response: new Promise(async res => {
-				worker = await createWorker('eng', 1, {cachePath: this.ai.options.path});
+				worker = await createWorker(this.ai.options.tesseract?.model || 'eng', 2, {cachePath: this.ai.options.path});
 				const {data} = await worker.recognize(path);
 				await worker.terminate();
 				res(data.text.trim() || null);

vite.config.ts

@@ -1,12 +1,19 @@
 import {defineConfig} from 'vite';
 import dts from 'vite-plugin-dts';
+import {resolve} from 'path';
 
 export default defineConfig({
 	build: {
 		lib: {
-			entry: './src/index.ts',
+			entry: {
+				index: './src/index.ts',
+				embedder: './src/embedder.ts',
+			},
 			name: 'utils',
-			fileName: (format) => (format === 'es' ? 'index.mjs' : 'index.js'),
+			fileName: (format, entryName) => {
+				if (entryName === 'embedder') return 'embedder.js';
+				return format === 'es' ? 'index.mjs' : 'index.js';
+			},
 		},
 		ssr: true,
 		emptyOutDir: true,