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### Description Adding branch support for RemoteTable by threading a branch selector onto every operation the data plane accepts it on. Exposes the currentBranch to nodejs and python through the bindings. Matching the server handlers, the branch rides as: - a `?branch=` query parameter for Arrow-body and query-only ops (insert, merge_insert, multipart_*, version/list, drop_index) - a `branch` field in the JSON body for everything else (count_rows, query, update, delete, create_index, column ops, index list/stats, stats, restore, describe, tags create/update) A main-branch handle (`branch == None`) produces byte-identical requests to before: no `branch` field and no `?branch=` - Handle-per-branch: `create_branch` / `checkout_branch` return a new handle with fresh caches and reset version/freshness state, mirroring `NativeTable`. - `create_branch` maps 409 to already-exists, 400 to invalid, and 404 to not-found with source context, and sends without retry so the 409 stays observable. - `Ref` translation covers version, version-number (relative to the handle's branch), and tag (resolved via the tags endpoint); `"main"` and empty normalize to the main branch. - Python branch handles persist their branch (and pinned version) across pickle/fork, so a forked or pickled handle reopens on its branch rather than silently reverting to main. ### Tests - Rust mock tests per op category (query-param and body mechanisms, branch CRUD, error paths, backward-compat). - Python sync branch CRUD, `open_table(branch=)`, and a pickle round-trip regression test.
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.