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lancedb-gatefixer[bot]
ae81d73563
<!-- lance-gatekeeper-fix:v1 agent=d30696bc46eb32f04c9927b0792e35d3 generation=1 --> ## Summary - use the Lance native batch KNN path so fixed-size batch vector searches share one flat table scan - validate consistent query-vector dimensions and retain the per-vector plan when offsets require its existing semantics - add Rust and Python regressions and update Rust, Python, and TypeScript API documentation ## Root cause LanceDB expanded every vector in a batch into a separate scan plan and joined the plans with `UnionExec`. For unindexed tables on S3, a batch of ten vectors therefore ran ten concurrent full scans, amplifying CPU and retained data enough to produce the reported memory spike. The native Lance batch KNN path performs bounded-memory selection for all query vectors over one flat scan. LanceDB now supplies the vectors as a batch and avoids applying a global scanner limit to the combined per-query results. Batch queries with a nonzero offset keep the previous plan because the native batch API does not support per-query offsets. ## Validation - targeted Rust batch-query plan and execution tests - `cargo check --quiet --features remote --tests --examples` - `cargo clippy --quiet --features remote --tests --examples` - `cargo fmt --all -- --check` - targeted Python batch-vector regression after rebuilding the extension - Ruff formatting/checks for the touched Python files - Node.js build, lint, docs generation, and targeted batch-vector Jest test - `git diff --check` Fixes #2468 --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io>
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.