laurus-nodejs

Node.js/TypeScript bindings for the Laurus search library — unified lexical, vector, and hybrid search.

Features

• Lexical search — BM25 scoring with Term, Phrase, Fuzzy, Wildcard, Geo, Boolean, and Span queries
• Vector search — HNSW, Flat, and IVF indexes with multiple distance metrics
• Hybrid search — Combine lexical and vector search with RRF or Weighted Sum fusion
• CJK support — Japanese, Chinese, and Korean tokenization via Lindera
• Native performance — Rust core via napi-rs, no C API overhead
• TypeScript types — Auto-generated .d.ts type definitions

Installation

npm install laurus-nodejs

Quick Start

import { Index, Schema } from "laurus-nodejs";

// Define schema
const schema = new Schema();
schema.addTextField("title");
schema.addTextField("body");
schema.setDefaultFields(["title", "body"]);

// Create an in-memory index
const index = await Index.create(null, schema);

// Index documents
await index.putDocument("doc1", {
  title: "Rust Programming",
  body: "Safety and speed.",
});
await index.putDocument("doc2", {
  title: "Python Basics",
  body: "Versatile language.",
});
await index.commit();

// Search with DSL string
const results = await index.search("programming", 5);
for (const r of results) {
  console.log(r.id, r.score, r.document.title);
}

API Overview

Index

// Create index (in-memory or file-based)
const index = await Index.create();                    // in-memory
const index = await Index.create("./myindex", schema); // persistent

// Document CRUD
await index.putDocument("id", { field: "value" });     // upsert
await index.addDocument("id", { field: "chunk" });     // append (RAG)
const docs = await index.getDocuments("id");
await index.deleteDocuments("id");
await index.commit();

// Search
const results = await index.search("query DSL", limit, offset);
const results = await index.searchTerm("field", "term", limit);
const results = await index.searchVector("field", [0.1, ...], limit);
const results = await index.searchVectorText("field", "text", limit);
const results = await index.searchWithRequest(searchRequest);

// Stats
const stats = index.stats();
// { documentCount: 42, vectorFields: {
//     embedding: { count: 42, dimension: 384 }
// } }

Schema

const schema = new Schema();
schema.addTextField("title", true, true, false, "lindera-ipadic");
schema.addIntegerField("year");
schema.addFloatField("price");
schema.addBooleanField("active");
schema.addDatetimeField("created_at");
schema.addGeoField("location");
schema.addBytesField("thumbnail");
schema.addHnswField("embedding", 384, "cosine", 16, 200, "bert");
schema.addFlatField("embedding", 384);
schema.addIvfField("embedding", 384, "cosine", 100, 1);
schema.addEmbedder("bert", {
  type: "candle_bert",
  model: "sentence-transformers/all-MiniLM-L6-v2",
});
schema.setDefaultFields(["title", "body"]);

Search Request (Advanced)

import { SearchRequest } from "laurus-nodejs";

const req = new SearchRequest(10, 0);  // limit, offset
req.setQueryDsl("title:hello");
req.setLexicalTermQuery("body", "programming");
req.setLexicalPhraseQuery("title", ["machine", "learning"]);
req.setVectorQuery("embedding", [0.1, 0.2, ...]);
req.setVectorTextQuery("embedding", "query text");
req.setFilterQuery("category", "tech");
req.setRrfFusion(60.0);
req.setWeightedSumFusion(0.3, 0.7);

const results = await index.searchWithRequest(req);

Text Analysis

import { WhitespaceTokenizer, SynonymDictionary, SynonymGraphFilter } from "laurus-nodejs";

const tokenizer = new WhitespaceTokenizer();
const tokens = tokenizer.tokenize("hello world");

const synDict = new SynonymDictionary();
synDict.addSynonymGroup(["ml", "machine learning"]);

const filter = new SynonymGraphFilter(synDict, true, 0.8);
const expanded = filter.apply(tokens);

Data Types

| JavaScript | Laurus Field Type | | --- | --- | | string | Text | | number (integer) | Int64 | | number (float) | Float64 | | boolean | Boolean | | null | Null | | number[] | Vector | | { lat, lon } | Geo | | Date / ISO8601 string | DateTime | | Buffer | Bytes |

Examples

See the examples/ directory:

• quickstart.mjs — Basic index, document, and search
• lexical-search.mjs — All lexical query types
• vector-search.mjs — Vector search with HNSW
• hybrid-search.mjs — Hybrid search with RRF and WeightedSum fusion

Building from Source

cd laurus-nodejs
npm install
npm run build        # release build
npm run build:debug  # debug build
npm test             # run tests

License

MIT