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Yang Cen
a9028ef441
Lance beta.11 calibrates lower `search_effort` tiers by metric and requested `k`. With the Rust dependency and Python/Node execution tests already on main via #4881, document the tiers across Rust, Python, Node and the search guide, extend the remote request matrix to cover `0.1` and `0.25`, and align the Python test package with beta.11. `0.1` and `0.25` target empirical mean partition-routing recall of 80% and 90% for L2, cosine and dot through `k=100000`. These are not per-query or final Recall@k guarantees. Default Auto is unchanged; Hamming and larger `k` remain uncalibrated, and remote queries require corresponding server support. This PR makes no additional performance claim. See [Lance #10695](https://github.com/lance-format/lance/pull/10695) for measurements and limitations. Validation: both strict workspace Clippy configurations, Rust formatting, 13 Rust effort tests, 208 Python query tests, 41 Node query tests, native binding builds, TypeScript, Node lint/generated docs, Python lint/format, full lockfile check and two dependency guards passed on `a8b09170`. All validated Rust/Python/Node source and dependency files are byte-identical at `2a82d07c`; the only subsequent change restores the Java POM to main. Root Ruff has 59 existing diagnostics and six formatting files, all byte-identical to the base; the Python package passes. The Java REST/Namespace client has no changed API here, and its dependency remains identical to main. The separate [Java beta publication failure](https://github.com/lance-format/lance/issues/10773) is not claimed resolved or validated. Official SDK release, automatic mirror, Sophon adoption and measured consumer acceptance remain follow-up stages.
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.