Image Features

import { transformers } from '@localmode/transformers';
import { extractImageFeatures, cosineSimilarity } from '@localmode/core';

const model = transformers.imageFeatures('Xenova/siglip-base-patch16-224');
const SIMILARITY_THRESHOLD = 0.85;

async function findDuplicates(images: string[]) {
  // Extract features for all images
  const allFeatures: Float32Array[] = [];
  for (const img of images) {
    const { features } = await extractImageFeatures({
      model,
      image: img,
      abortSignal: controller.signal,
    });
    allFeatures.push(features);
  }

  // Compare all pairs
  const duplicates: [number, number, number][] = [];
  for (let i = 0; i < allFeatures.length; i++) {
    for (let j = i + 1; j < allFeatures.length; j++) {
      const similarity = cosineSimilarity(allFeatures[i], allFeatures[j]);
      if (similarity > SIMILARITY_THRESHOLD) {
        duplicates.push([i, j, similarity]);
      }
    }
  }

  return duplicates;
}

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

const imageModel = transformers.imageFeatures('Xenova/siglip-base-patch16-224');
const textModel = transformers.embedding('Xenova/siglip-base-patch16-224');

const db = await createVectorDB({
  name: 'gallery',
  dimensions: 768,
  storage: 'memory',
});

// Index images by their features
async function indexImage(id: string, imageDataUrl: string) {
  const { features } = await extractImageFeatures({
    model: imageModel,
    image: imageDataUrl,
  });
  await db.add({ id, vector: features, metadata: { fileName: id } });
}

// Search by text query (cross-modal)
async function searchByText(query: string, topK = 10) {
  const { embedding } = await embed({ model: textModel, value: query });
  return db.search(embedding, { k: topK });
}