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Valentin","url":"http://www.mihaivalentin.com"},"license":"MPL-1.1","homepage":"https://github.com/MihaiValentin/lunr-languages#readme","repository":{"type":"git","url":"git+https://github.com/MihaiValentin/lunr-languages.git"},"description":"A a collection of languages stemmers and stopwords for Lunr Javascript library","maintainers":[{"name":"mihaivalentin","email":"mihaivalentin+npmjs@gmail.com"}],"readme":"# Lunr Languages — Multilingual Search for AI, RAG & Local-First Apps\n\n> **Used by 18k+ projects • ~300k weekly downloads**\n\nLunr Languages is an extension for Lunr.js that enables **fast, multilingual full-text search** across dozens of languages — in the browser or Node.js.\n\nOriginally built for classic search, it is now widely used as a **lightweight retrieval layer in AI systems**, including:\n\n* Retrieval-Augmented Generation (RAG)\n* Hybrid search (keyword + vector)\n* Local-first / edge AI apps\n* Static site search and documentation search\n\n⭐ If this project saves you time or powers something important, consider starring it or supporting its maintenance.\n\n---\n\n## Supported Languages\n\n*  German\n*  French\n*  Spanish2\n*  Italian\n*  Dutch\n*  Danish\n*  Portuguese\n*  Polish\n*  Finnish\n*  Romanian\n*  Hungarian\n*  Russian\n*  Norwegian\n*  Swedish\n*  Turkish\n*  Japanese\n*  Thai\n*  Arabic\n*  Chinese1\n*  Vietnamese\n*  Sanskrit\n*  Kannada\n*  Telugu\n*  Hindi\n*  Tamil\n*  Korean\n*  Armenian\n*  Hebrew\n*  Greek\n\n→ [Contribute a new language](CONTRIBUTING.md)\n\n---\n\n1 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.\n\n2 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.\n\n---\n\n## Why Lunr Languages in an AI world?\n\nModern AI systems don’t replace search — they **depend on it**.\n\nBefore an LLM can generate an answer, it needs **relevant context**. That’s where Lunr Languages fits:\n\n### 🔎 Fast and consistent lexical retrieval\n\nFilter thousands of documents down to a small candidate set before embedding or reranking.\n\n### 🌍 Multilingual support out of the box\n\nTokenization, stemming, and stopwords for 30+ languages — still a hard problem in AI pipelines.\n\n### ⚡ Zero infrastructure\n\nRuns entirely in the browser or Node.js. No vector DB required.\n\n### 🔒 Privacy-friendly / offline-ready\n\nPerfect for:\n\n* in-browser AI assistants\n* local knowledge bases\n* on-device search\n\n---\n\n## Example: Hybrid Search (Keyword + AI)\n\n```text\nUser query\n→ Lunr (keyword search, multilingual)\n→ top 100–500 documents\n→ embeddings / reranker\n→ LLM generates answer\n```\n\nLunr Languages improves **recall and precision**, especially for:\n\n* non-English content\n* inflected languages\n* mixed-language datasets\n\n---\n\n## Installation\n\n```bash\nnpm install lunr-languages\n```\n\n---\n\n## Usage\n\n### Basic example (German)\n\n```javascript\nconst lunr = require('lunr');\nrequire('lunr-languages/lunr.stemmer.support')(lunr);\nrequire('lunr-languages/lunr.de')(lunr);\n\nconst idx = lunr(function () {\n this.use(lunr.de);\n\n this.field('title', { boost: 10 });\n this.field('body');\n\n this.add({ title: 'Dokument', body: 'Beispieltext' });\n});\n```\n\nThe existing CommonJS usage is still supported. Modern runtimes and TypeScript projects can import the same subpath modules:\n\n```javascript\nimport lunr from 'lunr';\nimport stemmerSupport from 'lunr-languages/lunr.stemmer.support';\nimport de from 'lunr-languages/lunr.de';\n\nstemmerSupport(lunr);\nde(lunr);\n```\n\nType declarations are included for the registration modules and the Lunr properties they install, including `lunr.multiLanguage`.\n\nFor Spanish indexes on Lunr 2, you can opt into accent-insensitive matching by expanding accented tokens before stemming:\n\n```js\nrequire('lunr-languages/lunr.stemmer.support')(lunr);\nrequire('lunr-languages/lunr.es')(lunr);\n\nvar idx = lunr(function () {\n this.use(lunr.es);\n this.pipeline.before(lunr.es.stemmer, lunr.es.accentFold);\n this.searchPipeline.before(lunr.es.stemmer, lunr.es.accentFold);\n\n this.field('body');\n this.add({ id: 1, body: 'Respiración' });\n});\n```\n\n---\n\n## Multi-language indexing\n\n```javascript\nrequire('lunr-languages/lunr.multi')(lunr);\n\nconst idx = lunr(function () {\n this.use(lunr.multiLanguage('en', 'ru', 'de'));\n\n this.field('title');\n this.field('body');\n});\n```\n\n---\n\n## Chinese Tokenization\n\nChinese support is designed to work without mandatory native binaries:\n\n* 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.\n* 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.\n* If neither `@node-rs/jieba` nor `Intl.Segmenter` is available in Node.js, Chinese tokenization logs an error and throws.\n\nThe `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.\n\nTo opt into Jieba tokenization in Node.js:\n\n```bash\nnpm install @node-rs/jieba\n```\n\n---\n\n## Where this fits in modern architectures\n\nLunr Languages is commonly used as:\n\n* **Pre-filter for vector search**\n* **Fallback when embeddings fail**\n* **Client-side retrieval for AI apps**\n* **Static / documentation search**\n\n👉 In practice, **hybrid search (keyword + vector) performs best**\n\n---\n\n## How it works\n\nTo provide high-quality search across languages:\n\n* **Tokenization** — language-aware splitting (including Japanese, Chinese, etc.)\n* **Stemming** — matches different word forms\n* **Stopword filtering** — removes noise\n* **Trimming** — normalizes tokens\n\nThese steps improve both **classic search** and **AI retrieval pipelines**.\n\n---\n\n## When to use Lunr Languages vs vector search\n\nUse Lunr Languages when you need:\n\n* fast, deterministic keyword matching\n* multilingual normalization\n* offline / browser-based search\n* low-cost retrieval\n\nCombine with embeddings for:\n\n* semantic similarity\n* fuzzy concept matching\n\n---\n\n## Contributing\n\nWant to add a new language?\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md)\n\n---\n\n## Support / Sponsorship\n\nMaintained as an open-source project for over a decade.\n\nIf your company relies on this in production:\n\n* consider sponsoring\n* or contributing improvements\n\nIt helps keep the ecosystem stable.\n\n---\n\n## Final note\n\nEven in an AI-first world, **retrieval is the bottleneck**.\n\nLunr Languages ensures the *right* content reaches your models — fast, locally, and across languages.\n","readmeFilename":"README.md","users":{"antixrist":true,"mihaivalentin":true}}