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Fixes #2716 ## Summary Add support for querying with Float16Array, Float64Array, and Uint8Array vectors in the Node.js SDK, eliminating precision loss from the previous \Float32Array.from()\ conversion. ## Implementation Follows @wjones127's [5-step plan](https://github.com/lancedb/lancedb/issues/2716#issuecomment-3447750543): ### Rust (\ odejs/src/query.rs\) 1. \ytes_to_arrow_array(data: Uint8Array, dtype: String)\ helper that: - Creates an Arrow \Buffer\ from the raw bytes - Wraps it in a typed \ScalarBuffer<T>\ based on the dtype enum - Constructs a \PrimitiveArray\ and returns \Arc<dyn Array>\ 2. \ earest_to_raw(data, dtype)\ and \dd_query_vector_raw(data, dtype)\ NAPI methods that pass the type-erased array to the core \ earest_to\/\dd_query_vector\ which already accept \impl IntoQueryVector\ for \Arc<dyn Array>\ ### TypeScript (\ odejs/lancedb/query.ts\, \rrow.ts\) 3. Extended \IntoVector\ type to include \Uint8Array\ (and \Float16Array\ via runtime check for Node 22+) 4. \xtractVectorBuffer()\ helper detects non-Float32 typed arrays and extracts their underlying byte buffer + dtype string 5. \ earestTo()\ and \ddQueryVector()\ route through the raw NAPI path when the input is Float16/Float64/Uint8 ### Backward compatibility Existing \Float32Array\ and \ umber[]\ inputs are unchanged -- they still use the original \ earest_to(Float32Array)\ NAPI method. The new raw path is only used when a non-Float32 typed array is detected. ## Usage \\\ ypescript // Float16Array (Node 22+) -- no precision loss const f16vec = new Float16Array([0.1, 0.2, 0.3]); const results = await table.query().nearestTo(f16vec).limit(10).toArray(); // Float64Array -- no precision loss const f64vec = new Float64Array([0.1, 0.2, 0.3]); const results = await table.query().nearestTo(f64vec).limit(10).toArray(); // Uint8Array (binary embeddings) const u8vec = new Uint8Array([1, 0, 1, 1, 0]); const results = await table.query().nearestTo(u8vec).limit(10).toArray(); // Existing usage unchanged const results = await table.query().nearestTo([0.1, 0.2, 0.3]).limit(10).toArray(); \\\ ## Note on dependencies The Rust side uses \rrow_array\, \rrow_buffer\, and \half\ crates. These should already be in the dependency tree via \lancedb\ core, but \Cargo.toml\ may need explicit entries for \half\ and the arrow sub-crates in the nodejs workspace. --------- Signed-off-by: Vedant Madane <6527493+VedantMadane@users.noreply.github.com> Co-authored-by: Will Jones <willjones127@gmail.com>
The Multimodal AI Lakehouse
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The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
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Follow the Quickstart doc to set up LanceDB locally.
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| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
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