nodejs client for the liftwing LaBSE embeddings
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Authored by santhosh on Feb 9 2026, 5:28 AM.

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	F71762568: nodejs client for the liftwing LaBSE embeddings
	Feb 9 2026, 5:28 AM

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	#!/usr/bin/env node

	const http = require("http");

	/**
	* Get embedding vector from the API
	* @param {string[]} text - Text to get embedding for
	* @returns {Promise<number[]>} - Embedding vector
	*/
	async function getEmbedding(texts) {
	const postData = JSON.stringify({
	input: texts,
	});

	const options = {
	hostname: "localhost",
	port: 8080,
	path: "/v1/models/labse-embedding:predict",
	method: "POST",
	headers: {
	"Content-Type": "application/json",
	"Content-Length": Buffer.byteLength(postData),
	},
	};

	return new Promise((resolve, reject) => {
	const req = http.request(options, (res) => {
	let data = "";

	res.on("data", (chunk) => {
	data += chunk;
	});

	res.on("end", () => {
	try {
	const response = JSON.parse(data);
	// Adjust this based on the actual API response structure
	const embeddings = response.data;
	resolve(embeddings);
	} catch (error) {
	reject(new Error(`Failed to parse response: ${error.message}`));
	}
	});
	});

	req.on("error", (error) => {
	reject(new Error(`API request failed: ${error.message}`));
	});

	req.write(postData);
	req.end();
	});
	}
	/**
	* Calculate cosine similarity between two normalized vectors
	* @param {number[]} vec1 - First normalized vector
	* @param {number[]} vec2 - Second normalized vector
	* @returns {number} - Similarity score between -1 and 1
	*/
	function cosineSimilarity(vec1, vec2) {
	if (vec1.length !== vec2.length) {
	throw new Error("Vectors must have the same length");
	}

	let dotProduct = 0;
	for (let i = 0; i < vec1.length; i++) {
	dotProduct += vec1[i] * vec2[i];
	}

	return dotProduct;
	}

	/**
	* Compare two strings for similarity using embeddings
	* @param {string} str1 - First string to compare
	* @param {string} str2 - Second string to compare
	* @returns {Promise<number>} - Similarity score between 0 and 1
	*/
	async function compareStrings(str1, str2) {
	console.log("Getting embeddings ...");
	const embeddings = await getEmbedding([str1, str2]);
	console.log("Calculating similarity...");
	const similarity = cosineSimilarity(
	embeddings[0].embedding,
	embeddings[1].embedding,
	);

	return similarity;
	}

	// Main execution
	async function main() {
	const args = process.argv.slice(2);

	if (args.length < 2) {
	console.error("Usage: node compare-strings.js <string1> <string2>");
	console.error('Example: node compare-strings.js "Hello world" "Hi there"');
	process.exit(1);
	}

	const string1 = args[0];
	const string2 = args[1];

	console.log(`\nComparing strings:`);
	console.log(`String 1: "${string1}"`);
	console.log(`String 2: "${string2}"\n`);

	try {
	const similarity = await compareStrings(string1, string2);
	console.log(
	`\nSimilarity score: ${similarity.toFixed(4)} (${(similarity * 100).toFixed(2)}%)`,
	);
	} catch (error) {
	console.error(`Error: ${error.message}`);
	process.exit(1);
	}
	}

	main();

Event Timeline

santhosh created this paste.Feb 9 2026, 5:28 AM

santhosh mentioned this in T416659: Host sentence-transformers/LaBSE model in LiftWing.Feb 9 2026, 5:34 AM

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nodejs client for the liftwing LaBSE embeddings
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