feat(python): support to_pydantic in async (#2438)

This request improves support for `pydantic` integration by adding
`to_pydantic` method to asynchronous queries and handling models that
use `alias` in field definitions. Fixes #2436 and closes #2437 .

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- Added support for converting asynchronous query results to Pydantic
models.
- **Bug Fixes**
- Simplified conversion of query results to Pydantic models for improved
reliability.
- Improved handling of field aliases and computed fields when mapping
query results to Pydantic models.
- **Tests**
- Added tests to verify correct mapping of aliased and computed fields
in both synchronous and asynchronous scenarios.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

python/python/lancedb/query.py

@@ -786,10 +786,7 @@ class LanceQueryBuilder(ABC):
         -------
         List[LanceModel]
         """
-        return [
-            model(**{k: v for k, v in row.items() if k in model.field_names()})
-            for row in self.to_arrow(timeout=timeout).to_pylist()
-        ]
+        return [model(**row) for row in self.to_arrow(timeout=timeout).to_pylist()]
 
     def to_polars(self, *, timeout: Optional[timedelta] = None) -> "pl.DataFrame":
         """
@@ -2400,6 +2397,28 @@ class AsyncQueryBase(object):
 
         return pl.from_arrow(await self.to_arrow(timeout=timeout))
 
+    async def to_pydantic(
+        self, model: Type[LanceModel], *, timeout: Optional[timedelta] = None
+    ) -> List[LanceModel]:
+        """
+        Convert results to a list of pydantic models.
+
+        Parameters
+        ----------
+        model : Type[LanceModel]
+            The pydantic model to use.
+        timeout : timedelta, optional
+            The maximum time to wait for the query to complete.
+            If None, wait indefinitely.
+
+        Returns
+        -------
+        list[LanceModel]
+        """
+        return [
+            model(**row) for row in (await self.to_arrow(timeout=timeout)).to_pylist()
+        ]
+
     async def explain_plan(self, verbose: Optional[bool] = False):
         """Return the execution plan for this query.
 

python/python/tests/test_pydantic.py

@@ -412,3 +412,50 @@ def test_multi_vector_in_lance_model():
 
     t = TestModel(id=1)
     assert t.vectors == [[0.0] * 16]
+
+
+def test_aliases_in_lance_model(mem_db):
+    data = [
+        {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+        {"vector": [5.9, 6.5], "item": "bar", "price": 20.0},
+    ]
+    tbl = mem_db.create_table("items", data=data)
+
+    class TestModel(LanceModel):
+        name: str = Field(alias="item")
+        price: float
+        distance: float = Field(alias="_distance")
+
+    model = (
+        tbl.search([5.9, 6.5])
+        .distance_type("cosine")
+        .limit(1)
+        .to_pydantic(TestModel)[0]
+    )
+    assert hasattr(model, "name")
+    assert hasattr(model, "distance")
+    assert model.distance < 0.01
+
+
+@pytest.mark.asyncio
+async def test_aliases_in_lance_model_async(mem_db_async):
+    data = [
+        {"vector": [8.3, 2.5], "item": "foo", "price": 12.0},
+        {"vector": [7.7, 3.9], "item": "bar", "price": 11.2},
+    ]
+    tbl = await mem_db_async.create_table("items", data=data)
+
+    class TestModel(LanceModel):
+        name: str = Field(alias="item")
+        price: float
+        distance: float = Field(alias="_distance")
+
+    model = (
+        await tbl.vector_search([7.7, 3.9])
+        .distance_type("cosine")
+        .limit(1)
+        .to_pydantic(TestModel)
+    )[0]
+    assert hasattr(model, "name")
+    assert hasattr(model, "distance")
+    assert model.distance < 0.01