Used by 18k+ projects • ~300k weekly downloads

Lunr Languages is an extension for Lunr.js that enables fast, multilingual full-text search across dozens of languages — in the browser or Node.js.

Originally built for classic search, it is now widely used as a lightweight retrieval layer in AI systems, including:

• Retrieval-Augmented Generation (RAG)
• Hybrid search (keyword + vector)
• Local-first / edge AI apps
• Static site search and documentation search

⭐ If this project saves you time or powers something important, consider starring it or supporting its maintenance.

• German
• French
• Spanish²
• Italian
• Dutch
• Danish
• Portuguese
• Polish
• Finnish
• Romanian
• Hungarian
• Russian
• Norwegian
• Swedish
• Turkish
• Japanese
• Thai
• Arabic
• Chinese¹
• Vietnamese
• Sanskrit
• Kannada
• Telugu
• Hindi
• Tamil
• Korean
• Armenian
• Hebrew
• Greek

→ Contribute a new language

¹ Chinese tokenization uses Intl.Segmenter with CJK bigrams by default, which works in modern browsers and Node.js without native dependencies. In Node.js, if @node-rs/jieba is installed, Lunr Languages uses it automatically for higher-quality Jieba segmentation. Browsers must support Intl.Segmenter; there is no frontend fallback.

² Spanish includes an opt-in lunr.es.accentFold pipeline function for Lunr 2 indexes that should match user queries with omitted accents, such as respiracion matching Respiración, without replacing the default Spanish stemmer.

Modern AI systems don’t replace search — they depend on it.

Before an LLM can generate an answer, it needs relevant context. That’s where Lunr Languages fits:

Filter thousands of documents down to a small candidate set before embedding or reranking.

Tokenization, stemming, and stopwords for 30+ languages — still a hard problem in AI pipelines.

Runs entirely in the browser or Node.js. No vector DB required.

Perfect for:

• in-browser AI assistants
• local knowledge bases
• on-device search

User query
→ Lunr (keyword search, multilingual)
→ top 100–500 documents
→ embeddings / reranker
→ LLM generates answer

Lunr Languages improves recall and precision, especially for:

• non-English content
• inflected languages
• mixed-language datasets

npm install lunr-languages

const lunr = require('lunr');
require('lunr-languages/lunr.stemmer.support')(lunr);
require('lunr-languages/lunr.de')(lunr);

const idx = lunr(function () {
  this.use(lunr.de);

  this.field('title', { boost: 10 });
  this.field('body');

  this.add({ title: 'Dokument', body: 'Beispieltext' });
});

The existing CommonJS usage is still supported. Modern runtimes and TypeScript projects can import the same subpath modules:

import lunr from 'lunr';
import stemmerSupport from 'lunr-languages/lunr.stemmer.support';
import de from 'lunr-languages/lunr.de';

stemmerSupport(lunr);
de(lunr);

Type declarations are included for the registration modules and the Lunr properties they install, including lunr.multiLanguage.

For Spanish indexes on Lunr 2, you can opt into accent-insensitive matching by expanding accented tokens before stemming:

require('lunr-languages/lunr.stemmer.support')(lunr);
require('lunr-languages/lunr.es')(lunr);

var idx = lunr(function () {
  this.use(lunr.es);
  this.pipeline.before(lunr.es.stemmer, lunr.es.accentFold);
  this.searchPipeline.before(lunr.es.stemmer, lunr.es.accentFold);

  this.field('body');
  this.add({ id: 1, body: 'Respiración' });
});

require('lunr-languages/lunr.multi')(lunr);

const idx = lunr(function () {
  this.use(lunr.multiLanguage('en', 'ru', 'de'));

  this.field('title');
  this.field('body');
});

Chinese support is designed to work without mandatory native binaries:

• In browsers, lunr.zh uses Intl.Segmenter plus CJK bigrams. If Intl.Segmenter is unavailable, it logs an error and throws because there is no bundled browser fallback.
• In Node.js, lunr.zh first tries to load @node-rs/jieba. If it is installed, it is used for better Chinese segmentation. If it is not installed, Lunr Languages logs an informational message and falls back to Intl.Segmenter plus CJK bigrams.
• If neither @node-rs/jieba nor Intl.Segmenter is available in Node.js, Chinese tokenization logs an error and throws.

The Intl.Segmenter fallback avoids native package supply-chain risk and works well for lightweight search, but it is not identical to Jieba. Bigrams improve recall for common two-character search terms such as 车主 and 学姐, while Jieba generally provides better precision and ranking for serious Chinese search.

To opt into Jieba tokenization in Node.js:

npm install @node-rs/jieba

Lunr Languages is commonly used as:

• Pre-filter for vector search
• Fallback when embeddings fail
• Client-side retrieval for AI apps
• Static / documentation search

👉 In practice, hybrid search (keyword + vector) performs best

To provide high-quality search across languages:

• Tokenization — language-aware splitting (including Japanese, Chinese, etc.)
• Stemming — matches different word forms
• Stopword filtering — removes noise
• Trimming — normalizes tokens

These steps improve both classic search and AI retrieval pipelines.

Use Lunr Languages when you need:

• fast, deterministic keyword matching
• multilingual normalization
• offline / browser-based search
• low-cost retrieval

Combine with embeddings for:

• semantic similarity
• fuzzy concept matching

Want to add a new language?

See CONTRIBUTING.md

Maintained as an open-source project for over a decade.

If your company relies on this in production:

• consider sponsoring
• or contributing improvements

It helps keep the ecosystem stable.

Even in an AI-first world, retrieval is the bottleneck.

Lunr Languages ensures the right content reaches your models — fast, locally, and across languages.