feat(node): support Float16, Float64, and Uint8 vector queries (#3193)

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>

nodejs/__test__/vector_types.test.ts

@@ -0,0 +1,110 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import * as tmp from "tmp";
+
+import { type Table, connect } from "../lancedb";
+import {
+  Field,
+  FixedSizeList,
+  Float32,
+  Int64,
+  Schema,
+  makeArrowTable,
+} from "../lancedb/arrow";
+
+describe("Vector query with different typed arrays", () => {
+  let tmpDir: tmp.DirResult;
+
+  afterEach(() => {
+    tmpDir?.removeCallback();
+  });
+
+  async function createFloat32Table(): Promise<Table> {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+    const db = await connect(tmpDir.name);
+    const schema = new Schema([
+      new Field("id", new Int64(), true),
+      new Field(
+        "vec",
+        new FixedSizeList(2, new Field("item", new Float32())),
+        true,
+      ),
+    ]);
+    const data = makeArrowTable(
+      [
+        { id: 1n, vec: [1.0, 0.0] },
+        { id: 2n, vec: [0.0, 1.0] },
+        { id: 3n, vec: [1.0, 1.0] },
+      ],
+      { schema },
+    );
+    return db.createTable("test_f32", data);
+  }
+
+  it("should search with Float32Array (baseline)", async () => {
+    const table = await createFloat32Table();
+    const results = await table
+      .query()
+      .nearestTo(new Float32Array([1.0, 0.0]))
+      .limit(1)
+      .toArray();
+
+    expect(results.length).toBe(1);
+    expect(Number(results[0].id)).toBe(1);
+  });
+
+  it("should search with number[] (backward compat)", async () => {
+    const table = await createFloat32Table();
+    const results = await table
+      .query()
+      .nearestTo([1.0, 0.0])
+      .limit(1)
+      .toArray();
+
+    expect(results.length).toBe(1);
+    expect(Number(results[0].id)).toBe(1);
+  });
+
+  it("should search with Float64Array via raw path", async () => {
+    const table = await createFloat32Table();
+    const results = await table
+      .query()
+      .nearestTo(new Float64Array([1.0, 0.0]))
+      .limit(1)
+      .toArray();
+
+    expect(results.length).toBe(1);
+    expect(Number(results[0].id)).toBe(1);
+  });
+
+  it("should add multiple query vectors with Float64Array", async () => {
+    const table = await createFloat32Table();
+    const results = await table
+      .query()
+      .nearestTo(new Float64Array([1.0, 0.0]))
+      .addQueryVector(new Float64Array([0.0, 1.0]))
+      .limit(2)
+      .toArray();
+
+    expect(results.length).toBeGreaterThanOrEqual(2);
+  });
+
+  // Float16Array is only available in Node 22+; not in TypeScript's standard lib yet
+  const float16ArrayCtor = (globalThis as unknown as Record<string, unknown>)
+    .Float16Array as (new (values: number[]) => unknown) | undefined;
+  const hasFloat16 = float16ArrayCtor !== undefined;
+  const f16it = hasFloat16 ? it : it.skip;
+
+  f16it("should search with Float16Array via raw path", async () => {
+    const table = await createFloat32Table();
+    const results = await table
+      .query()
+      .nearestTo(new float16ArrayCtor!([1.0, 0.0]) as Float32Array)
+      .limit(1)
+      .toArray();
+
+    expect(results.length).toBe(1);
+    expect(Number(results[0].id)).toBe(1);
+  });
+});