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defVector(dim:int,value_type:pa.DataType=pa.float32(),nullable:bool=True)->Type[FixedSizeListMixin]:"""Pydantic Vector Type.    !!! warning        Experimental feature.    Parameters    ----------    dim : int        The dimension of the vector.    value_type : pyarrow.DataType, optional        The value type of the vector, by default pa.float32()    nullable : bool, optional        Whether the vector is nullable, by default it is True.    Examples    --------    >>> import pydantic    >>> from lancedb.pydantic import Vector    ...    >>> class MyModel(pydantic.BaseModel):    ...     id: int    ...     url: str    ...     embeddings: Vector(768)    >>> schema = pydantic_to_schema(MyModel)    >>> assert schema == pa.schema([    ...     pa.field("id", pa.int64(), False),    ...     pa.field("url", pa.utf8(), False),    ...     pa.field("embeddings", pa.list_(pa.float32(), 768))    ... ])    """# TODO: make a public parameterized type.classFixedSizeList(list,FixedSizeListMixin):def__repr__(self):returnf"FixedSizeList(dim={dim})"@staticmethoddefnullable()->bool:returnnullable@staticmethoddefdim()->int:returndim@staticmethoddefvalue_arrow_type()->pa.DataType:returnvalue_type@classmethoddef__get_pydantic_core_schema__(cls,_source_type:Any,_handler:pydantic.GetCoreSchemaHandler)->CoreSchema:returncore_schema.no_info_after_validator_function(cls,core_schema.list_schema(min_length=dim,max_length=dim,items_schema=core_schema.float_schema(),),)@classmethoddef__get_validators__(cls)->Generator[Callable,None,None]:yieldcls.validate# For pydantic v1@classmethoddefvalidate(cls,v):ifnotisinstance(v,(list,range,np.ndarray))orlen(v)!=dim:raiseTypeError("A list of numbers or numpy.ndarray is needed")returncls(v)ifPYDANTIC_VERSION.major<2:@classmethoddef__modify_schema__(cls,field_schema:Dict[str,Any]):field_schema["items"]={"type":"number"}field_schema["maxItems"]=dimfield_schema["minItems"]=dimreturnFixedSizeList

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defpydantic_to_schema(model:Type[pydantic.BaseModel])->pa.Schema:"""Convert a [Pydantic Model][pydantic.BaseModel] to a       [PyArrow Schema][pyarrow.Schema].    Parameters    ----------    model : Type[pydantic.BaseModel]        The Pydantic BaseModel to convert to Arrow Schema.    Returns    -------    pyarrow.Schema        The Arrow Schema    Examples    --------    >>> from typing import List, Optional    >>> import pydantic    >>> from lancedb.pydantic import pydantic_to_schema, Vector    >>> class FooModel(pydantic.BaseModel):    ...     id: int    ...     s: str    ...     vec: Vector(1536)  # fixed_size_list<item: float32>[1536]    ...     li: List[int]    ...    >>> schema = pydantic_to_schema(FooModel)    >>> assert schema == pa.schema([    ...     pa.field("id", pa.int64(), False),    ...     pa.field("s", pa.utf8(), False),    ...     pa.field("vec", pa.list_(pa.float32(), 1536)),    ...     pa.field("li", pa.list_(pa.int64()), False),    ... ])    """fields=_pydantic_model_to_fields(model)returnpa.schema(fields)