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## Summary - Add an issue-specific regression for appending generated embeddings to an empty table with a non-nullable vector field. - Verify the custom embedding function produces the declared Float64 vectors and both appended rows are readable. ## Root cause In v0.4.19, records without a vector value were materialized against the explicit schema before embeddings were inserted. Apache Arrow inferred the generated batch vector field as nullable while the table retained the user-provided non-nullable field, then rejected the mismatched schemas. The current conversion path excludes the generated field from the initial record conversion and realigns the completed batch to the stored schema after embedding, but the reported empty-table append sequence lacked permanent regression coverage. ## Validation - `pnpm exec biome format --write __test__/embedding.test.ts` - `pnpm lint-ci` - `pnpm test -- --runInBand __test__/embedding.test.ts` (12 passed, 1 skipped integration test) - `pnpm build` - `pnpm run docs` Fixes #1281 <!-- lance-gatekeeper-fix:v1 agent=6b7270aeb92e6b6c6f5b45022fa83f6a generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
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.