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type-safe utilities for vector embedding comparison and search.","maintainers":[{"name":"allemandi","email":"myallemandi@outlook.com"}],"readme":"# ๐Ÿ“– @allemandi/embed-utils\n\n[![NPM Version](https://img.shields.io/npm/v/@allemandi/embed-utils)](https://www.npmjs.com/package/@allemandi/embed-utils)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/allemandi/embed-utils/blob/main/LICENSE)\n\n> **Fast, type-safe utilities for vector embedding comparison and search.**\n>\n> Works in Node.js, browsers โ€“ supports ESM, CommonJS, and UMD\n \n\n## ๐Ÿ”– Table of Contents\n- [โœจ Features](#-features)\n- [๐Ÿ› ๏ธ Installation](#๏ธ-installation)\n- [๐Ÿš€ Quick Usage Examples](#-quick-usage-examples)\n- [๐Ÿ“š API Reference](#-api-reference)\n - [computeCosineSimilarity](#computecosinesimilarity)\n - [computeEuclideanDistance](#computeeuclideandistance)\n - [computeManhattanDistance](#computemanhattandistance)\n - [normalizeVector](#normalizevector)\n - [isNormalized](#isnormalized)\n - [meanVector](#meanvector)\n - [findNearestNeighbors](#findnearestneighbors)\n - [rankBySimilarity](#rankbysimilarity)\n- [๐Ÿงช Tests](#-tests)\n- [๐Ÿ”— Related Projects](#-related-projects)\n- [๐Ÿค Contributing](#-contributing)\n\n\n## โœจ Features\n\n- ๐Ÿ” Find nearest neighbors by cosine similarity, or Euclidean/Manhattan distance\n- ๐Ÿ“ Compute, normalize, and verify vector similarity\n- โšก Lightweight and fast vector operations\n\n## ๐Ÿ› ๏ธ Installation\n```bash\n# Yarn\nyarn add @allemandi/embed-utils\n\n# NPM\nnpm install @allemandi/embed-utils\n```\n\n## ๐Ÿš€ Quick Usage Examples\n\n**ESM**\n```js\nimport { computeCosineSimilarity } from '@allemandi/embed-utils';\n```\n**CommonJS**\n\n```js\nconst { findNearestNeighbors } = require('@allemandi/embed-utils');\n\nconst samples = [\n { embedding: [0.1, 0.2, 0.3], label: 'sports' },\n { embedding: [0.9, 0.8, 0.7], label: 'finance' },\n { embedding: [0.05, 0.1, 0.15], label: 'sports' },\n];\n\nconst query = [0.09, 0.18, 0.27];\n\n// Find top 2 neighbors with similarity โ‰ฅ 0.5\n// (default method: cosine similarity)\nconst resultsCosine = findNearestNeighbors(query, samples, { topK: 2, threshold: 0.5 });\n\nconsole.log(resultsCosine);\n// [ { embedding: [0.1, 0.2, 0.3], label: \"sports\", similarityScore: 1 },\n// { embedding: [0.05, 0.1, 0.15], label: \"sports\", similarityScore: 1 } ] \n\n// Find top 3 neighbors with Euclidean distance โ‰ค 1.1\nconst resultsEuclidean = findNearestNeighbors(query, samples, {\n topK: 3,\n threshold: 1.1,\n method: 'euclidean',\n});\n\nconsole.log(resultsEuclidean.length);\n// 2\n// only 2 results that pass threshold conditions\n\n```\n**UMD (Browser)**\n```js\n\n\n```\n\n## ๐Ÿ“š API Reference\n\n### computeCosineSimilarity\n\nCalculates cosine similarity between two vectors.\nMeasures how similar their directions are, ignoring magnitude.\nUse for comparing semantic or normalized vectors (e.g., text embeddings).\n\n**Parameters:**\n* `vecA` (`number[]`): First vector.\n* `vecB` (`number[]`): Second vector.\n\n**Returns:** `number` โ€” Cosine similarity score between `vecA` and `vecB`.\n\n**Examples:**\n```js\ncomputeCosineSimilarity([1, 2, 3], [1, 2, 3]);\n// => 1 (identical vectors)\ncomputeCosineSimilarity([1, 0], [0, 1]);\n// => 0 (orthogonal vectors)\ncomputeCosineSimilarity([1, 2], [2, 3]);\n// => 0.992...\ncomputeCosineSimilarity([1, 0], [-1, 0]);\n// => -1 (vectors diametrically opposed)\ncomputeCosineSimilarity([0, 0], [1, 2]);\n// => 0 (one vector has zero magnitude)\n```\n\n---\n\n### computeEuclideanDistance\n\nCalculates Euclidean distance between two vectors.\nMeasures straight-line distance considering both magnitude and direction.\nUse for raw numeric data or spatial coordinates.\n\n**Parameters:**\n* `vecA` (`number[]`): First vector.\n* `vecB` (`number[]`): Second vector.\n\n**Returns:** `number` โ€” Euclidean distance between `vecA` and `vecB`.\n\n**Examples:**\n```js\ncomputeEuclideanDistance([1, 2], [4, 6]);\n// => 5 (distance between (1,2) and (4,6))\ncomputeEuclideanDistance([0, 0], [0, 0]);\n// => 0 (identical vectors)\ncomputeEuclideanDistance([1, 0], [0, 1]);\n// => 1.414...\ncomputeEuclideanDistance([1, 2, 3], [4, 5, 6]);\n// => 5.196...\n```\n\n---\n\n### computeManhattanDistance\n\nCalculates Manhattan distance between two vectors.\nMeasures sum of absolute differences.\nUse for grid-like data or when less sensitive to large differences.\n\n**Parameters:**\n* `vecA` (`number[]`): First vector.\n* `vecB` (`number[]`): Second vector.\n\n**Returns:** `number` โ€” Manhattan distance between `vecA` and `vecB`.\n\n**Examples:**\n```js\ncomputeManhattanDistance([1, 2, 3], [4, 5, 6]);\n// => 9\ncomputeManhattanDistance([1, 0], [0, 1]);\n// => 2\ncomputeManhattanDistance([1, 2], [1, 2]);\n// => 0 (identical vectors)\ncomputeManhattanDistance([1, -1], [-1, 1]);\n// => 4\n```\n\n---\n\n### normalizeVector\n\nNormalizes a vector to unit length. If the vector has zero magnitude, returns the original vector.\n\n**Parameters:**\n* `vec` (`number[]`): Input vector.\n\n**Returns:** `number[]` โ€” A new vector scaled to unit length.\n\n**Examples:**\n```js\nnormalizeVector([3, 4]);\n// => [0.6, 0.8] (vector normalized to length 1)\nnormalizeVector([0, 0]);\n// => [0, 0] (zero vector remains unchanged)\nnormalizeVector([1, 1, 1]);\n// => [0.5773502691896258, 0.5773502691896258, 0.5773502691896258]\n```\n\n---\n\n### isNormalized\n\nEfficiently checks if a vector is L2-normalized (unit length).\n\n**Parameters:**\n* `vec` (`number[]`): Input vector.\n* `epsilon` (`number`, optional, default: `1e-6`): Tolerance for floating-point comparison.\n\n**Returns:** `boolean` โ€” True if the L2 norm is within epsilon of 1.\n\n**Examples:**\n```js\nisNormalized([1, 0]);\n// => true (vector length is exactly 1)\nisNormalized([0.6, 0.8]);\n// => true (approximately unit length)\nisNormalized([3, 4]);\n// => false (length is 5)\nisNormalized([0, 0]);\n// => false (length is 0)\n```\n\n---\n\n### meanVector\n\nComputes the mean (centroid) vector from an array of vectors.\nAssumes all vectors are of equal length.\n\n**Parameters:**\n* `vectors` (`number[][]`): An array of vectors.\n\n**Returns:** `number[]` โ€” The mean vector.\n\n**Examples:**\n```js\nmeanVector([[1, 2], [3, 4], [5, 6]]);\n// => [3, 4]\nmeanVector([]);\n// => []\n```\n\n---\n\n### findNearestNeighbors\n\nFinds the nearest neighbors to a given query embedding from a list of samples based on the specified distance/similarity method.\n\n* `'cosine'`: Cosine similarity (higher = more similar, range: [-1, 1]).\n* `'euclidean'`: Euclidean distance (lower = closer, โ‰ฅ 0).\n* `'manhattan'`: Manhattan distance (lower = closer, โ‰ฅ 0).\n\n**Parameters:**\n* `queryEmbedding` (`number[]`): The embedding vector to compare against.\n* `samples` (`Array<{ embedding: number[], label: string }>`): An array of samples, each with an `embedding` and a `label`.\n* `options` (`object`, optional, default: `{}`):\n * `options.topK` (`number`, optional, default: `1`): Number of top results to return.\n * `options.threshold` (`number`, optional): Minimum similarity score threshold for results (cosine) or maximum distance threshold (euclidean/manhattan).\n * `options.method` (`'cosine' | 'euclidean' | 'manhattan'`, optional, default: `'cosine'`): The metric to compute.\n\n**Examples:**\n```js\nconst samples = [\n { embedding: [1, 0], label: 'A' },\n { embedding: [0, 1], label: 'B' },\n { embedding: [1, 1], label: 'C' },\n];\n\n// Default cosine similarity\nfindNearestNeighbors([1, 0], samples);\n// => [{ embedding: [1, 0], label: 'A', similarityScore: 1 }]\n\n// Euclidean distance\nfindNearestNeighbors([1, 0], samples, { method: 'euclidean', topK: 2 });\n// => [\n// { embedding: [1, 0], label: 'A', distance: 0 },\n// { embedding: [1, 1], label: 'C', distance: 1 }\n// ]\n\n// Manhattan distance with threshold\nfindNearestNeighbors([1, 0], samples, { method: 'manhattan', threshold: 1.5 });\n// => [{ embedding: [1, 0], label: 'A', distance: 0 }, { embedding: [1, 1], label: 'C', distance: 1 }]\n\n// Cosine with threshold\nfindNearestNeighbors([1, 0], samples, { threshold: 0.9 });\n// => [{ embedding: [1, 0], label: 'A', similarityScore: 1 }]\n```\n\n---\n\n### rankBySimilarity\n\nRanks all samples by similarity/distance to the query embedding.\nDoes NOT apply threshold or topK filtering.\n\n**Parameters:**\n* `queryEmbedding` (`number[]`): The embedding vector to compare against.\n* `samples` (`Array<{ embedding: number[], label: string }>`): Samples with embeddings and labels.\n* `options` (`object`, optional, default: `{}`):\n * `options.method` (`'cosine' | 'euclidean' | 'manhattan'`, optional, default: `'cosine'`): Distance/similarity method to use.\n\n**Examples:**\n```js\nconst samples = [\n { embedding: [1, 0], label: 'A' },\n { embedding: [0, 1], label: 'B' },\n { embedding: [1, 1], label: 'C' },\n];\n\n// Default cosine similarity\nrankBySimilarity([1, 0], samples);\n// => [\n// { embedding: [1, 0], label: 'A', similarityScore: 1 },\n// { embedding: [1, 1], label: 'C', similarityScore: 0.707... },\n// { embedding: [0, 1], label: 'B', similarityScore: 0 }\n// ]\n\n// Euclidean distance\nrankBySimilarity([1, 0], samples, { method: 'euclidean' });\n// => [\n// { embedding: [1, 0], label: 'A', distance: 0 },\n// { embedding: [1, 1], label: 'C', distance: 1 },\n// { embedding: [0, 1], label: 'B', distance: 1.414... }\n// ]\n\n// Manhattan distance\nrankBySimilarity([0, 1], samples, { method: 'manhattan' });\n// => [\n// { embedding: [0, 1], label: 'B', distance: 0 },\n// { embedding: [1, 1], label: 'C', distance: 1 },\n// { embedding: [1, 0], label: 'A', distance: 2 }\n// ]\n```\n\n## ๐Ÿงช Tests\n\n> Available in the GitHub repo only.\n\n```bash\n# Run the test suite with Jest\nyarn test\n# or\nnpm test\n```\n\n## ๐Ÿ”— Related Projects\nCheck out these related projects that might interest you:\n\n**[Embed Classify CLI](https://github.com/allemandi/embed-classify-cli)**\n- Node.js CLI tool for local text classification using word embeddings.\n\n**[Vector Knowledge Base](https://github.com/allemandi/vector-knowledge-base)** \n- A minimalist command-line knowledge system with semantic memory capabilities using vector embeddings for information retrieval.\n\n\n## ๐Ÿค Contributing\nIf you have ideas, improvements, or new features:\n\n1. Fork the project\n2. Create your feature branch (git checkout -b feature/amazing-feature)\n3. Commit your changes (git commit -m 'Add some amazing feature')\n4. Push to the branch (git push origin feature/amazing-feature)\n5. Open a Pull Request\n","readmeFilename":"README.md"}