import { embed, embedMany } from '@localmode/core';
import { transformers } from '@localmode/transformers';

// Create embedding model
const embeddingModel = transformers.embedding('Xenova/all-MiniLM-L6-v2');

// Single embedding
const { embedding } = await embed({
  model: embeddingModel,
  value: 'Hello world',
});

// Batch embeddings
const { embeddings } = await embedMany({
  model: embeddingModel,
  values: ['Hello', 'World', 'How are you?'],
});

import { rerank } from '@localmode/core';
import { transformers } from '@localmode/transformers';

const rerankerModel = transformers.reranker('Xenova/ms-marco-MiniLM-L-6-v2');

const { results } = await rerank({
  model: rerankerModel,
  query: 'What is machine learning?',
  documents: ['ML is a subset of AI...', 'Python is a language...', 'Neural networks...'],
  topK: 5,
});

console.log(results); // Sorted by relevance score

import { preloadModel, isModelCached, getModelStorageUsage } from '@localmode/transformers';

// Check if model is cached
const cached = await isModelCached('Xenova/all-MiniLM-L6-v2');

// Preload model with progress
await preloadModel('Xenova/all-MiniLM-L6-v2', {
  onProgress: (p) => console.log(`${p.progress}% loaded`),
});

// Check storage usage
const usage = await getModelStorageUsage();

import { classify, extractEntities } from '@localmode/core';
import { transformers } from '@localmode/transformers';

// Text Classification
const sentiment = await classify({
  model: transformers.classifier('Xenova/distilbert-base-uncased-finetuned-sst-2-english'),
  text: 'I love this product!',
});
console.log(sentiment.label); // 'POSITIVE'

// Named Entity Recognition
const entities = await extractEntities({
  model: transformers.ner('Xenova/bert-base-NER'),
  text: 'John works at Microsoft in Seattle',
});
console.log(entities.entities);
// [{ entity: 'John', type: 'PER', ... }, { entity: 'Microsoft', type: 'ORG', ... }, ...]

import { transcribe, synthesizeSpeech } from '@localmode/core';
import { transformers } from '@localmode/transformers';

// Speech-to-Text
const transcription = await transcribe({
  model: transformers.speechToText('Xenova/whisper-tiny'),
  audio: audioBlob,
  returnTimestamps: true,
});
console.log(transcription.text);

// Text-to-Speech
const { audio, sampleRate } = await synthesizeSpeech({
  model: transformers.textToSpeech('Xenova/speecht5_tts'),
  text: 'Hello, how are you?',
});

import { classifyImage, captionImage } from '@localmode/core';
import { transformers } from '@localmode/transformers';

// Image Classification
const classification = await classifyImage({
  model: transformers.imageClassifier('Xenova/vit-base-patch16-224'),
  image: imageBlob,
});
console.log(classification.predictions);

// Image Captioning
const caption = await captionImage({
  model: transformers.captioner('Xenova/blip-image-captioning-base'),
  image: imageBlob,
});
console.log(caption.caption);

const model = transformers.embedding('Xenova/all-MiniLM-L6-v2', {
  quantized: true, // Use quantized model (smaller, faster)
  device: 'webgpu', // Use WebGPU for acceleration (falls back to WASM)
});

const { embedding } = await embed({
  model: transformers.embedding('Xenova/all-MiniLM-L6-v2'),
  value: 'Hello world',
  providerOptions: {
    transformers: {
      // Any Transformers.js specific options
    },
  },
});

import { preloadModel, isModelCached } from '@localmode/transformers';
import { embed } from '@localmode/core';

// Check and preload if needed
if (!(await isModelCached('Xenova/all-MiniLM-L6-v2'))) {
  await preloadModel('Xenova/all-MiniLM-L6-v2', {
    onProgress: (p) => console.log(`Loading: ${p.progress}%`),
  });
}

// Subsequent calls are instant (loaded from cache)
const embeddingModel = transformers.embedding('Xenova/all-MiniLM-L6-v2');
const { embedding } = await embed({ model: embeddingModel, value: 'Hello' });