feat: search multiple query vectors as one query (#1811)

Allows users to pass multiple query vector as part of a single query
plan. This just runs the queries in parallel without any further
optimization. It's mostly a convenience.

Previously, I think this was only handled by the sync Python remote API.
This makes it common across all SDKs.

Closes https://github.com/lancedb/lancedb/issues/1803

```python
>>> import lancedb
>>> import asyncio
>>> 
>>> async def main():
...     db = await lancedb.connect_async("./demo")
...     table = await db.create_table("demo", [{"id": 1, "vector": [1, 2, 3]}, {"id": 2, "vector": [4, 5, 6]}], mode="overwrite")
...     return await table.query().nearest_to([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [4.0, 5.0, 6.0]]).limit(1).to_pandas()
... 
>>> asyncio.run(main())
   query_index  id           vector  _distance
0            2   2  [4.0, 5.0, 6.0]        0.0
1            1   2  [4.0, 5.0, 6.0]        0.0
2            0   1  [1.0, 2.0, 3.0]        0.0
```

python/src/query.rs

@@ -142,6 +142,13 @@ impl VectorQuery {
         self.inner = self.inner.clone().only_if(predicate);
     }
 
+    pub fn add_query_vector(&mut self, vector: Bound<'_, PyAny>) -> PyResult<()> {
+        let data: ArrayData = ArrayData::from_pyarrow_bound(&vector)?;
+        let array = make_array(data);
+        self.inner = self.inner.clone().add_query_vector(array).infer_error()?;
+        Ok(())
+    }
+
     pub fn select(&mut self, columns: Vec<(String, String)>) {
         self.inner = self.inner.clone().select(Select::dynamic(&columns));
     }