Files
lancedb/python/tests/test_pydantic.py
Chang She 9a192426d3 feat(python): support list of list fields from pydantic schema (#747)
For object detection, each row may correspond to an image and each image
can have multiple bounding boxes of x-y coordinates. This means that a
`bbox` field is potentially "list of list of float". This adds support
in our pydantic-pyarrow conversion for nested lists.
2024-04-05 16:25:02 -07:00

213 lines
6.0 KiB
Python

# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import sys
from datetime import date, datetime
from typing import List, Optional, Tuple
import pyarrow as pa
import pydantic
import pytest
from pydantic import Field
from lancedb.pydantic import PYDANTIC_VERSION, LanceModel, Vector, pydantic_to_schema
@pytest.mark.skipif(
sys.version_info < (3, 9),
reason="using native type alias requires python3.9 or higher",
)
def test_pydantic_to_arrow():
class StructModel(pydantic.BaseModel):
a: str
b: Optional[float]
class TestModel(pydantic.BaseModel):
id: int
s: str
vec: list[float]
li: List[int]
lili: List[List[float]]
litu: List[Tuple[float, float]]
opt: Optional[str] = None
st: StructModel
dt: date
dtt: datetime
# d: dict
m = TestModel(
id=1,
s="hello",
vec=[1.0, 2.0, 3.0],
li=[2, 3, 4],
lili=[[2.5, 1.5], [3.5, 4.5], [5.5, 6.5]],
litu=[(2.5, 1.5), (3.5, 4.5), (5.5, 6.5)],
st=StructModel(a="a", b=1.0),
dt=date.today(),
dtt=datetime.now(),
)
schema = pydantic_to_schema(TestModel)
expect_schema = pa.schema(
[
pa.field("id", pa.int64(), False),
pa.field("s", pa.utf8(), False),
pa.field("vec", pa.list_(pa.float64()), False),
pa.field("li", pa.list_(pa.int64()), False),
pa.field("lili", pa.list_(pa.list_(pa.float64())), False),
pa.field("litu", pa.list_(pa.list_(pa.float64())), False),
pa.field("opt", pa.utf8(), True),
pa.field(
"st",
pa.struct(
[pa.field("a", pa.utf8(), False), pa.field("b", pa.float64(), True)]
),
False,
),
pa.field("dt", pa.date32(), False),
pa.field("dtt", pa.timestamp("us"), False),
]
)
assert schema == expect_schema
def test_pydantic_to_arrow_py38():
class StructModel(pydantic.BaseModel):
a: str
b: Optional[float]
class TestModel(pydantic.BaseModel):
id: int
s: str
vec: List[float]
li: List[int]
lili: List[List[float]]
litu: List[Tuple[float, float]]
opt: Optional[str] = None
st: StructModel
dt: date
dtt: datetime
# d: dict
m = TestModel(
id=1,
s="hello",
vec=[1.0, 2.0, 3.0],
li=[2, 3, 4],
lili=[[2.5, 1.5], [3.5, 4.5], [5.5, 6.5]],
litu=[(2.5, 1.5), (3.5, 4.5), (5.5, 6.5)],
st=StructModel(a="a", b=1.0),
dt=date.today(),
dtt=datetime.now(),
)
schema = pydantic_to_schema(TestModel)
expect_schema = pa.schema(
[
pa.field("id", pa.int64(), False),
pa.field("s", pa.utf8(), False),
pa.field("vec", pa.list_(pa.float64()), False),
pa.field("li", pa.list_(pa.int64()), False),
pa.field("lili", pa.list_(pa.list_(pa.float64())), False),
pa.field("litu", pa.list_(pa.list_(pa.float64())), False),
pa.field("opt", pa.utf8(), True),
pa.field(
"st",
pa.struct(
[pa.field("a", pa.utf8(), False), pa.field("b", pa.float64(), True)]
),
False,
),
pa.field("dt", pa.date32(), False),
pa.field("dtt", pa.timestamp("us"), False),
]
)
assert schema == expect_schema
def test_fixed_size_list_field():
class TestModel(pydantic.BaseModel):
vec: Vector(16)
li: List[int]
data = TestModel(vec=list(range(16)), li=[1, 2, 3])
if PYDANTIC_VERSION >= (2,):
assert json.loads(data.model_dump_json()) == {
"vec": list(range(16)),
"li": [1, 2, 3],
}
else:
assert data.dict() == {
"vec": list(range(16)),
"li": [1, 2, 3],
}
schema = pydantic_to_schema(TestModel)
assert schema == pa.schema(
[
pa.field("vec", pa.list_(pa.float32(), 16), False),
pa.field("li", pa.list_(pa.int64()), False),
]
)
if PYDANTIC_VERSION >= (2,):
json_schema = TestModel.model_json_schema()
else:
json_schema = TestModel.schema()
assert json_schema == {
"properties": {
"vec": {
"items": {"type": "number"},
"maxItems": 16,
"minItems": 16,
"title": "Vec",
"type": "array",
},
"li": {"items": {"type": "integer"}, "title": "Li", "type": "array"},
},
"required": ["vec", "li"],
"title": "TestModel",
"type": "object",
}
def test_fixed_size_list_validation():
class TestModel(pydantic.BaseModel):
vec: Vector(8)
with pytest.raises(pydantic.ValidationError):
TestModel(vec=range(9))
with pytest.raises(pydantic.ValidationError):
TestModel(vec=range(7))
TestModel(vec=range(8))
def test_lance_model():
class TestModel(LanceModel):
vector: Vector(16) = Field(default=[0.0] * 16)
li: List[int] = Field(default=[1, 2, 3])
schema = pydantic_to_schema(TestModel)
assert schema == TestModel.to_arrow_schema()
assert TestModel.field_names() == ["vector", "li"]
t = TestModel()
assert t == TestModel(vec=[0.0] * 16, li=[1, 2, 3])