Files
lancedb/nodejs
Will Jones ed6be12ad6 docs: clear the mkdocs warning backlog so --strict passes
`mkdocs build` emitted 61 warnings on main, and rendering the previously
undocumented classes in this PR pushed that to 158. That backlog is what
blocks turning on strict mode (#3707), so clear it here rather than leave
it worse than we found it.

Most of it was one systematic false positive: griffe cannot see the
generated `__init__` of a pydantic dataclass, so every documented
parameter looked unknown. `warn_unknown_params` turns that check off.

The rest were real docstring bugs, in 15 docstrings:

* Prose trailing a `Parameters` section is read as parameter names, which
  invented parameters called `The`, `you` and `To`. Moved into `Notes` or
  the summary.
* numpydoc only reads a type when the colon has spaces around it. Where
  the documented name is a pydantic attribute rather than a signature
  parameter, griffe has no signature to fall back on and the type was
  dropped. Affects nine embedding classes.
* `num_partitions, default sqrt(num_rows)` and friends parse as a list of
  names, rendering a bogus `default` parameter.
* One parameter indented five spaces instead of four.

`nodejs/CONTRIBUTING.md` links to the repo-root CONTRIBUTING.md, which
does not resolve once typedoc copies the file into `docs/src/js/_media/`;
an absolute URL works from both places.

`mkdocs build --strict` now exits 0.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-07-29 14:15:20 -07:00
..
2025-03-21 10:56:29 -07:00
2025-01-29 08:27:07 -08:00

LanceDB JavaScript SDK

A JavaScript library for LanceDB.

Installation

npm install @lancedb/lancedb

This will download the appropriate native library for your platform. We currently support:

  • Linux (x86_64 and aarch64 on glibc and musl)
  • MacOS (Intel and ARM/M1/M2)
  • Windows (x86_64 and aarch64)

Usage

Basic Example

import * as lancedb from "@lancedb/lancedb";
const db = await lancedb.connect("data/sample-lancedb");
const table = await db.createTable("my_table", [
  { id: 1, vector: [0.1, 1.0], item: "foo", price: 10.0 },
  { id: 2, vector: [3.9, 0.5], item: "bar", price: 20.0 },
]);
const results = await table.vectorSearch([0.1, 0.3]).limit(20).toArray();
console.log(results);

The quickstart contains more complete examples.

Development

See CONTRIBUTING.md for information on how to contribute to LanceDB.