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82 lines
2.4 KiB
Python
82 lines
2.4 KiB
Python
# Copyright 2023 LanceDB Developers
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import lance
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import numpy as np
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import pandas as pd
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import pandas.testing as tm
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import pyarrow as pa
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import pytest
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from lancedb.query import LanceQueryBuilder
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class MockTable:
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def __init__(self, tmp_path):
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self.uri = tmp_path
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def to_lance(self):
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return lance.dataset(self.uri)
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@pytest.fixture
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def table(tmp_path) -> MockTable:
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df = pd.DataFrame(
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{
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"vector": [[1, 2], [3, 4]],
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"id": [1, 2],
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"str_field": ["a", "b"],
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"float_field": [1.0, 2.0],
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}
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)
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schema = pa.schema(
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[
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pa.field("vector", pa.list_(pa.float32(), list_size=2)),
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pa.field("id", pa.int32()),
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pa.field("str_field", pa.string()),
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pa.field("float_field", pa.float64()),
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]
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)
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lance.write_dataset(df, tmp_path, schema)
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return MockTable(tmp_path)
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def test_query_builder(table):
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df = LanceQueryBuilder(table, [0, 0]).limit(1).select(["id"]).to_df()
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assert df["id"].values[0] == 1
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assert all(df["vector"].values[0] == [1, 2])
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def test_query_builder_with_filter(table):
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df = LanceQueryBuilder(table, [0, 0]).where("id = 2").to_df()
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assert df["id"].values[0] == 2
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assert all(df["vector"].values[0] == [3, 4])
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def test_query_builder_with_metric(table):
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query = [4, 8]
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df_default = LanceQueryBuilder(table, query).to_df()
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df_l2 = LanceQueryBuilder(table, query).metric("l2").to_df()
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tm.assert_frame_equal(df_default, df_l2)
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df_cosine = LanceQueryBuilder(table, query).metric("cosine").limit(1).to_df()
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assert df_cosine.score[0] == pytest.approx(
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cosine_distance(query, df_cosine.vector[0]),
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abs=1e-6,
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)
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assert 0 <= df_cosine.score[0] <= 1
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def cosine_distance(vec1, vec2):
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return 1 - np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
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