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/src/query.rs

@@ -3,6 +3,12 @@
 
 use std::sync::Arc;
 
+use arrow_array::{
+    Array, Float16Array as ArrowFloat16Array, Float32Array as ArrowFloat32Array,
+    Float64Array as ArrowFloat64Array, UInt8Array as ArrowUInt8Array,
+};
+use arrow_buffer::ScalarBuffer;
+use half::f16;
 use lancedb::index::scalar::{
     BooleanQuery, BoostQuery, FtsQuery, FullTextSearchQuery, MatchQuery, MultiMatchQuery, Occur,
     Operator, PhraseQuery,
@@ -24,6 +30,33 @@ use crate::rerankers::RerankHybridCallbackArgs;
 use crate::rerankers::Reranker;
 use crate::util::{parse_distance_type, schema_to_buffer};
 
+fn bytes_to_arrow_array(data: Uint8Array, dtype: String) -> napi::Result<Arc<dyn Array>> {
+    let buf = arrow_buffer::Buffer::from(data.to_vec());
+    let num_bytes = buf.len();
+    match dtype.as_str() {
+        "float16" => {
+            let scalar_buf = ScalarBuffer::<f16>::new(buf, 0, num_bytes / 2);
+            Ok(Arc::new(ArrowFloat16Array::new(scalar_buf, None)))
+        }
+        "float32" => {
+            let scalar_buf = ScalarBuffer::<f32>::new(buf, 0, num_bytes / 4);
+            Ok(Arc::new(ArrowFloat32Array::new(scalar_buf, None)))
+        }
+        "float64" => {
+            let scalar_buf = ScalarBuffer::<f64>::new(buf, 0, num_bytes / 8);
+            Ok(Arc::new(ArrowFloat64Array::new(scalar_buf, None)))
+        }
+        "uint8" => {
+            let scalar_buf = ScalarBuffer::<u8>::new(buf, 0, num_bytes);
+            Ok(Arc::new(ArrowUInt8Array::new(scalar_buf, None)))
+        }
+        _ => Err(napi::Error::from_reason(format!(
+            "Unsupported vector dtype: {}. Expected one of: float16, float32, float64, uint8",
+            dtype
+        ))),
+    }
+}
+
 #[napi]
 pub struct Query {
     inner: LanceDbQuery,
@@ -78,6 +111,13 @@ impl Query {
         Ok(VectorQuery { inner })
     }
 
+    #[napi]
+    pub fn nearest_to_raw(&mut self, data: Uint8Array, dtype: String) -> Result<VectorQuery> {
+        let array = bytes_to_arrow_array(data, dtype)?;
+        let inner = self.inner.clone().nearest_to(array).default_error()?;
+        Ok(VectorQuery { inner })
+    }
+
     #[napi]
     pub fn fast_search(&mut self) {
         self.inner = self.inner.clone().fast_search();
@@ -163,6 +203,13 @@ impl VectorQuery {
         Ok(())
     }
 
+    #[napi]
+    pub fn add_query_vector_raw(&mut self, data: Uint8Array, dtype: String) -> Result<()> {
+        let array = bytes_to_arrow_array(data, dtype)?;
+        self.inner = self.inner.clone().add_query_vector(array).default_error()?;
+        Ok(())
+    }
+
     #[napi]
     pub fn distance_type(&mut self, distance_type: String) -> napi::Result<()> {
         let distance_type = parse_distance_type(distance_type)?;