API Reference

Index

The main entry point for creating and querying search indexes.

Static Methods

Index.create(schema?)

Create a new in-memory (ephemeral) index.

• Parameters:
  • schema (Schema, optional) – Schema definition.
• Returns: Promise<Index>

Index.open(name, schema?)

Open or create a persistent index backed by OPFS.

• Parameters:
  • name (string) – Index name (OPFS subdirectory).
  • schema (Schema, optional) – Schema definition.
• Returns: Promise<Index>

Instance Methods

putDocument(id, document)

Replace a document (upsert).

• Parameters:
  • id (string) – Document identifier.
  • document (object) – Key-value pairs matching schema fields.
• Returns: Promise<void>

addDocument(id, document)

Append a document version (multi-version RAG pattern).

• Parameters / Returns: Same as putDocument.

getDocuments(id)

Retrieve all versions of a document.

• Parameters:
  • id (string)
• Returns: Promise<object[]>

deleteDocuments(id)

Delete all versions of a document.

• Parameters:
  • id (string)
• Returns: Promise<void>

commit()

Flush writes and make changes searchable. If opened with Index.open(), data is also persisted to OPFS.

• Returns: Promise<void>

search(query, limit?, offset?)

Search using a DSL string query.

• Parameters:
  • query (string) – Query DSL (e.g. "title:hello").
  • limit (number, default 10)
  • offset (number, default 0)
• Returns: Promise<SearchResult[]>

searchTerm(field, term, limit?, offset?)

Search for an exact term.

• Parameters:
  • field (string) – Field name.
  • term (string) – Exact term.
  • limit, offset (number, optional)
• Returns: Promise<SearchResult[]>

searchVector(field, vector, limit?, offset?)

Search by vector similarity.

• Parameters:
  • field (string) – Vector field name.
  • vector (number[]) – Query embedding.
  • limit, offset (number, optional)
• Returns: Promise<SearchResult[]>

searchVectorText(field, text, limit?, offset?)

Search by text (embedded by the registered embedder).

• Parameters:
  • field (string) – Vector field name.
  • text (string) – Text to embed.
  • limit, offset (number, optional)
• Returns: Promise<SearchResult[]>

searchGeo3dDistance(field, x, y, z, distanceM, limit?, offset?)

Sphere search over a 3D ECEF point field. Returns documents whose (x, y, z) coordinate is within distanceM metres of the centre. See Geo3d concepts for ECEF theory.

• Parameters:
  • field (string) – Geo3d field name.
  • x, y, z (number) – Centre ECEF coordinate (metres).
  • distanceM (number) – Maximum distance from the centre (metres).
  • limit, offset (number, optional)
• Returns: Promise<SearchResult[]>

searchGeo3dBoundingBox(field, minX, minY, minZ, maxX, maxY, maxZ, limit?, offset?)

Axis-aligned 3D bounding-box search over a 3D ECEF point field.

• Parameters:
  • field (string) – Geo3d field name.
  • minX, minY, minZ, maxX, maxY, maxZ (number) – Box bounds (metres).
  • limit, offset (number, optional)
• Returns: Promise<SearchResult[]>

searchGeo3dNearest(field, x, y, z, k, limit?, offset?, initialRadiusM?, maxRadiusM?)

k-nearest-neighbour search over a 3D ECEF point field. Returns the k documents closest to (x, y, z). The optional initialRadiusM and maxRadiusM parameters tune the iterative-expansion search cone.

• Parameters:
  • field (string) – Geo3d field name.
  • x, y, z (number) – Centre ECEF coordinate (metres).
  • k (number) – Number of nearest neighbours to return.
  • limit, offset (number, optional)
  • initialRadiusM, maxRadiusM (number, optional)
• Returns: Promise<SearchResult[]>

stats()

Return index statistics.

• Returns: { documentCount: number, vectorFields: { [name]: { count, dimension } } }

Schema

Builder for defining index fields and embedders.

Constructor

new Schema()

Create an empty schema.

Methods

addTextField(name, stored?, indexed?, termVectors?, analyzer?)

Add a full-text field. analyzer is the name of a parameter-less built-in ("standard", "english", "keyword", "simple", "noop") or the name of a runtime analyzer registered via addAnalyzer().

For Japanese morphological analysis, build a JapaneseAnalyzer from raw IPADIC bytes and register it with addAnalyzer() first; see JapaneseAnalyzer.fromBytes and addAnalyzer below.

addIntegerField(name, stored?, indexed?, multiValued?)

Add a 64-bit integer field. Pass multiValued: true to accept arrays of integers; range queries then match if any value satisfies the predicate (Lucene-style “any match” with constant scoring).

addFloatField(name, stored?, indexed?, multiValued?)

Add a 64-bit float field. Pass multiValued: true to accept arrays of floats; range queries then match if any value satisfies the predicate (Lucene-style “any match” with constant scoring).

addBooleanField(name, stored?, indexed?)

Add a boolean field.

addDatetimeField(name, stored?, indexed?)

Add a date/time field.

addGeoField(name, stored?, indexed?)

Add a geographic coordinate field.

addGeo3dField(name, stored?, indexed?)

Add a 3D ECEF Cartesian point field. Values are submitted as a { x, y, z } object with metres units. See Geo3d concepts for ECEF theory.

The WASM binding does not expose Geo3dDistanceQuery / Geo3dBoundingBoxQuery / Geo3dNearestQuery as JS classes (wasm-bindgen cannot expose dyn Query trait objects). Instead, use the Index.searchGeo3dDistance / Index.searchGeo3dBoundingBox / Index.searchGeo3dNearest methods documented above.

addBytesField(name, stored?)

Add a binary data field.

addHnswField(name, dimension, distance?, m?, efConstruction?, embedder?)

Add an HNSW vector index field.

• distance: "cosine" (default), "euclidean", "dot_product", "manhattan", "angular"
• m: Branching factor (default 16)
• efConstruction: Build-time expansion (default 200)

addFlatField(name, dimension, distance?, embedder?)

Add a brute-force vector index field.

addIvfField(name, dimension, distance?, nClusters?, nProbe?, embedder?)

Add an IVF vector index field.

• nClusters: Number of partitioning clusters (default 100)
• nProbe: Number of clusters to probe at query time (default 1)

addAnalyzer(name, analyzer)

Register a pre-built analyzer instance under name. Resolved before the parameter-less built-in names and before schema.analyzers definitions when text fields reference an analyzer by name.

Currently only JapaneseAnalyzer instances built via JapaneseAnalyzer.fromBytes are accepted here. The runtime registry is the only practical way to use the Japanese analyzer in browser WASM, where the { "language": "japanese", "dict": ... } preset cannot resolve a filesystem path.

import { JapaneseAnalyzer, Schema } from "laurus-wasm";
import { downloadDictionary, loadDictionaryFiles } from "laurus-wasm/opfs";

await downloadDictionary("./dict/lindera-ipadic.zip", "ipadic");
const f = await loadDictionaryFiles("ipadic");
const ja = JapaneseAnalyzer.fromBytes(
  f.metadata, f.dictDa, f.dictVals, f.dictWordsIdx,
  f.dictWords, f.matrixMtx, f.charDef, f.unk, "normal",
);

const schema = new Schema();
schema.addAnalyzer("ja-ipadic", ja);
schema.addTextField("body", undefined, undefined, undefined, "ja-ipadic");

addEmbedder(name, config)

Register a named embedder. WASM supports two type values:

• "precomputed" — No embedding is performed; vectors are passed directly via putDocument() / searchVector().
• "callback" — Provide a JavaScript callback embed: (text) => Promise<number[]> that the engine will invoke during ingestion and searchVectorText(). This enables in-engine auto-embedding using Transformers.js or any other in-browser embedding library.

// Precomputed embedder
schema.addEmbedder("precomputed-embedder", { type: "precomputed" });

// Callback embedder (e.g. backed by Transformers.js)
schema.addEmbedder("callback-embedder", {
  type: "callback",
  embed: async (text) => {
    const output = await pipeline(text, { pooling: "mean", normalize: true });
    return Array.from(output.data);
  },
});

setDefaultFields(fields)

Set the default search fields.

setDynamicFieldPolicy(policy)

Set how the engine treats fields that appear in ingested documents but are absent from the schema. policy is one of "strict", "dynamic" (default), or "ignore" (case-insensitive). Throws on an invalid value.

• "strict" — Reject the document.
• "dynamic" — Infer a type for each undeclared field and add it to the schema. Warning: integer fields silently truncate incoming float values (3.14 → 3).
• "ignore" — Silently drop the undeclared fields.

See Schema & Fields for the full behaviour matrix.

dynamicFieldPolicy()

Returns the current policy as a lowercase string.

fieldNames()

Returns an array of defined field names.

toString()

Returns a string representation of the schema ("Schema(fields=[...])").

SearchResult

interface SearchResult {
  id: string;
  score: number;
  document: object | null;
}

Analysis

JapaneseAnalyzer

Japanese morphological analyzer constructed from raw Lindera dictionary bytes. Browser WASM has no real filesystem, so the standard { "language": "japanese", "dict": "/path/to/ipadic" } preset cannot be used. Instead, fetch a Lindera dictionary archive (typically lindera-ipadic-X.Y.Z.zip), store it in OPFS via the OPFS helpers, and pass the eight component byte arrays to JapaneseAnalyzer.fromBytes.

JapaneseAnalyzer.fromBytes(metadata, dictDa, ..., mode?)

Static factory that builds an analyzer from raw IPADIC bytes.

Arguments (all Uint8Array except mode):

Throws if any component fails to deserialize or the mode string is invalid.

import { JapaneseAnalyzer } from "laurus-wasm";
import { loadDictionaryFiles } from "laurus-wasm/opfs";

const f = await loadDictionaryFiles("ipadic");
const ja = JapaneseAnalyzer.fromBytes(
  f.metadata, f.dictDa, f.dictVals, f.dictWordsIdx,
  f.dictWords, f.matrixMtx, f.charDef, f.unk,
  "normal",
);

The pipeline is NFKC normalization → Japanese iteration mark normalization → Lindera morphological tokenization → lowercase → Japanese stop word filter — identical to the japanese preset on the native side.

OPFS Helpers

The laurus-wasm/opfs subpath bundles helpers for downloading, storing, and loading Lindera dictionaries from the browser’s Origin Private File System. Used together with JapaneseAnalyzer.fromBytes.

import {
  downloadDictionary,
  loadDictionaryFiles,
  hasDictionary,
  listDictionaries,
  removeDictionary,
} from "laurus-wasm/opfs";

Browser CORS prevents fetching directly from GitHub Releases, so host the zip on the same origin as your app (the Laurus demo bundles ./dict/lindera-ipadic.zip alongside the WASM at deploy time).

WhitespaceTokenizer

const tokenizer = new WhitespaceTokenizer();
const tokens = tokenizer.tokenize("hello world");
// [{ text: "hello", position: 0, ... }, { text: "world", position: 1, ... }]

SynonymDictionary

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

SynonymGraphFilter

new SynonymGraphFilter(dictionary, keepOriginal = true, boost = 1.0)

• dictionary (SynonymDictionary) — Source synonym groups.
• keepOriginal (boolean, default true) — Keep the original token alongside the inserted synonyms.
• boost (number, default 1.0) — Score boost applied to inserted synonym tokens.

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