Why LXRT?

• Local-first: privacy, lower cost, lower latency.
• One infra layer: models, cache, workers, backend selection (WebGPU/WASM/Node), progress, vector store.
• Ready integrations: OpenAI-compatible, LangChain, React/Vue hooks.
• Multimodal: LLM, TTS, STT, Embeddings, OCR, audio/video/image vectorization for RAG.
• TypeScript-first: full typings, clear API.

Install

npm install lxrt @huggingface/transformers
# or yarn/pnpm equivalents

Quick start (LLM + cache + worker)

import { createAIProvider } from 'lxrt';

const provider = createAIProvider({
  llm: { model: 'onnx-community/Qwen2.5-0.5B-Instruct', dtype: 'q4' },
  backend: { prefer: 'webgpu', fallback: 'wasm' },
});

const reply = await provider.chat([
  { role: 'user', content: 'Hello, LXRT!' }
]);
console.log(reply);

Key capabilities

• LLM / Chat: local models, backend autoscaling, model cache.
• TTS / STT / OCR: text↔speech, speech recognition, OCR with workers and progress events.
• Embeddings & Vectorization: text, audio, image, video → vectors for RAG; local vector store (IndexedDB).
• OpenAI-compatible & LangChain: drop-in for existing clients.
• React / Vue: hooks/composables useAIProvider, useChat, useVectorization.
• Workers + Progress: heavy work off the main thread, progress events.

Examples

Embeddings (text)

const vec = await provider.embed(['hello world']);

Vectorization (file)

const result = await provider.vectorize(file, { modality: 'audio' });
console.log(result.vector, result.metadata);

OpenAI-compatible

import { OpenAIAdapter } from 'lxrt';

const client = new OpenAIAdapter(provider);
const resp = await client.chat.completions.create({
  model: 'local-llm',
  messages: [{ role: 'user', content: 'Hi!' }],
});

LangChain

import { createLangChainLLM } from 'lxrt';

const llm = createLangChainLLM(provider);
const res = await llm.invoke('Tell me a joke about cats');

React (hook)

import { useChat } from 'lxrt/react';
const { messages, sendMessage, isLoading } = useChat();

await sendMessage('Hi LXRT!');

Vue (composable)

import { useChat } from 'lxrt/vue';
const { messages, sendMessage, isLoading } = useChat();

Model download & progress

provider.on('progress', ({ file, progress }) => {
  console.log(`Downloading ${file}: ${progress}%`);
});

Configuration (quick view)

• Backend: webgpu / wasm / node (auto-fallback).
• Cache: automatic storage and reuse of models.
• Workers: all heavy computation in Web Workers; UI stays responsive.
• Vector Store: local IndexedDB for RAG/semantic search.

License

MIT