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[]>

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.

addIntegerField(name, stored?, indexed?)

Add an integer field.

addFloatField(name, stored?, indexed?)

Add a float field.

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.

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.

addEmbedder(name, config)

Register a named embedder. In WASM, only "precomputed" type is supported.

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

setDefaultFields(fields)

Set the default search fields.

fieldNames()

Returns an array of defined field names.

SearchResult

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

Analysis

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

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