embedding.js

import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/[email protected]';

const embed = await pipeline(
    "feature-extraction",
    "onnx-community/Qwen3-Embedding-0.6B-ONNX",
    { device: "webgpu", dtype: "q4f16" },
);

export async function getGroupEmbeddings(groups, task) {
    const groupEmbeddings = [];
    for (const g of groups) {
        // Remove lines starting with ##
        const lines = g.split(/\n/)
            .map(x => x.trim())
            .filter(x => x && !x.startsWith('##'));
        const prompts = lines.map(s => `Instruct: ${task}\nQuery:${s}`);
        const out = await embed(prompts, { pooling: "mean", normalize: true });
        const embeddings = typeof out.tolist === 'function' ? out.tolist() : out.data;
        const dim = embeddings[0].length;
        const avg = new Float32Array(dim);
        for (const e of embeddings) { for (let i = 0; i < dim; i++) avg[i] += e[i]; }
        for (let i = 0; i < dim; i++) avg[i] /= embeddings.length;
        groupEmbeddings.push(avg);
    }
    return groupEmbeddings;
}

export async function getLineEmbeddings(lines, task) {
    const prompts = lines.map(s => `Instruct: ${task}\nQuery:${s}`);
    const out = await embed(prompts, { pooling: "mean", normalize: true });
    return typeof out.tolist === 'function' ? out.tolist() : out.data;
}