fix(python)!: reject tuple fields with mixed element types in pydantic models (#4679)

`pydantic_to_schema` maps a `Tuple[...]` field to an Arrow list of its
**first** element type. A tuple with different element types gets the
wrong schema, and the error only surfaces when a row is written:

```python
class Pair(LanceModel):
    pair: Tuple[int, str]

pydantic_to_schema(Pair)        # pair: list<item: int64>
db.create_table("t", schema=Pair).add([Pair(pair=(1, "a"))])
# pyarrow.lib.ArrowInvalid: Could not convert 'a' with type str: tried to convert to int64
```

An Arrow list has a single element type, so such a tuple cannot be
stored as one. This raises a `TypeError` when the schema is built,
saying the tuple elements must share one type and pointing to a nested
`LanceModel` for a fixed set of differently typed fields. Homogeneous
tuples are unchanged: `Tuple[int, int]` and `Tuple[float, ...]` still
map to lists.

**Breaking change:** a model with a mixed-type tuple field, which used
to get a (wrong) `list<...>` schema from `pydantic_to_schema`, now
raises `TypeError` when the schema is built. Such a schema could not
hold the model's values: writing a row failed with `ArrowInvalid`. The
requirement and the nested-`LanceModel` alternative are documented in
the `pydantic_to_schema` docstring.

Tests (in `test_pydantic.py`): `TypeError` for `Tuple[int, str]` (fails
on `main`), and the list mapping for `Tuple[int, int]` and `Tuple[float,
...]`. The whole file passes; `ruff format` and `ruff check` are clean.

I used an AI assistant (Claude) while working on this; I reviewed and
tested the change.

---------

Co-authored-by: Xuanwo <github@xuanwo.io>

python/python/lancedb/pydantic.py

@@ -259,6 +259,15 @@ def _py_type_to_arrow_type(py_type: Type[Any], field: FieldInfo) -> pa.DataType:
                 f"{py_type}. Specify the element type, e.g. List[int] instead "
                 "of a bare List."
             )
+        # An Arrow list has a single element type, so a tuple such as
+        # Tuple[int, str] cannot be stored as one. Tuple[int, ...] is homogeneous.
+        element_types = {arg for arg in args if arg is not Ellipsis}
+        if len(element_types) > 1:
+            raise TypeError(
+                "Converting Pydantic type to Arrow Type: unsupported type "
+                f"{py_type}. All tuple elements must have the same type; use a "
+                "nested LanceModel for a fixed set of fields with different types."
+            )
         child = args[0]
         return _pydantic_list_child_to_arrow(child, field)
     raise TypeError(
@@ -419,6 +428,15 @@ def pydantic_to_schema(model: Type[pydantic.BaseModel]) -> pa.Schema:
     pyarrow.Schema
         The Arrow Schema
 
+    Raises
+    ------
+    TypeError
+        If a field has a type with no Arrow equivalent. A ``Tuple`` field becomes
+        an Arrow list, so all its elements must have the same type, as in
+        ``Tuple[int, int]`` or ``Tuple[float, ...]``; for a fixed set of fields
+        with different types, such as ``Tuple[int, str]``, use a nested
+        ``LanceModel`` (an Arrow struct) instead.
+
     Examples
     --------
 

python/python/tests/test_pydantic.py

@@ -200,6 +200,27 @@ def test_bare_generic_raises_type_error():
             pydantic_to_schema(TestModel)
 
 
+def test_tuple_with_mixed_element_types_raises_type_error():
+    # An Arrow list has one element type, so Tuple[int, str] has no Arrow
+    # equivalent. It used to map to list<int64> and fail later, on write.
+    class TestModel(pydantic.BaseModel):
+        pair: Tuple[int, str]
+
+    with pytest.raises(TypeError, match="same type"):
+        pydantic_to_schema(TestModel)
+
+
+def test_homogeneous_tuples_map_to_lists():
+    class TestModel(pydantic.BaseModel):
+        pair: Tuple[int, int]
+        many: Tuple[float, ...]
+
+    schema = pydantic_to_schema(TestModel)
+
+    assert schema.field("pair").type == pa.list_(pa.int64())
+    assert schema.field("many").type == pa.list_(pa.float64())
+
+
 def test_nested_struct_list_optional():
     class SplitInfo(pydantic.BaseModel):
         start_frame: int