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722462c38b
BREAKING CHANGE: The `score` column has been renamed to `_distance` to more accurately describe the semantics (smaller means closer / better). --------- Co-authored-by: Lei Xu <lei@lancedb.com>
148 lines
4.2 KiB
Python
148 lines
4.2 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 unittest.mock as mock
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import lance
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import numpy as np
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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.db import LanceDBConnection
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from lancedb.pydantic import LanceModel, vector
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from lancedb.query import LanceQueryBuilder, Query
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from lancedb.table import LanceTable
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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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self._conn = LanceDBConnection(self.uri)
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def to_lance(self):
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return lance.dataset(self.uri)
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def _execute_query(self, query):
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ds = self.to_lance()
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return ds.to_table(
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columns=query.columns,
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filter=query.filter,
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nearest={
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"column": query.vector_column,
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"q": query.vector,
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"k": query.k,
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"metric": query.metric,
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"nprobes": query.nprobes,
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"refine_factor": query.refine_factor,
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},
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)
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@pytest.fixture
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def table(tmp_path) -> MockTable:
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df = pa.table(
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{
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"vector": pa.array(
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[[1, 2], [3, 4]], type=pa.list_(pa.float32(), list_size=2)
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),
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"id": pa.array([1, 2]),
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"str_field": pa.array(["a", "b"]),
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"float_field": pa.array([1.0, 2.0]),
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}
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)
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lance.write_dataset(df, tmp_path)
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return MockTable(tmp_path)
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def test_cast(table):
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class TestModel(LanceModel):
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vector: vector(2)
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id: int
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str_field: str
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float_field: float
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q = LanceQueryBuilder(table, [0, 0], "vector").limit(1)
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results = q.to_pydantic(TestModel)
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assert len(results) == 1
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r0 = results[0]
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assert isinstance(r0, TestModel)
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assert r0.id == 1
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assert r0.vector == [1, 2]
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assert r0.str_field == "a"
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assert r0.float_field == 1.0
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def test_query_builder(table):
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df = LanceQueryBuilder(table, [0, 0], "vector").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], "vector").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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vector_column_name = "vector"
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df_default = LanceQueryBuilder(table, query, vector_column_name).to_df()
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df_l2 = LanceQueryBuilder(table, query, vector_column_name).metric("L2").to_df()
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tm.assert_frame_equal(df_default, df_l2)
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df_cosine = (
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LanceQueryBuilder(table, query, vector_column_name)
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.metric("cosine")
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.limit(1)
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.to_df()
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)
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assert df_cosine._distance[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._distance[0] <= 1
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def test_query_builder_with_different_vector_column():
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table = mock.MagicMock(spec=LanceTable)
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query = [4, 8]
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vector_column_name = "foo_vector"
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builder = (
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LanceQueryBuilder(table, query, vector_column_name)
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.metric("cosine")
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.where("b < 10")
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.select(["b"])
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.limit(2)
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)
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ds = mock.Mock()
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table.to_lance.return_value = ds
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builder.to_arrow()
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table._execute_query.assert_called_once_with(
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Query(
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vector=query,
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filter="b < 10",
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k=2,
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metric="cosine",
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columns=["b"],
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nprobes=20,
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refine_factor=None,
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vector_column="foo_vector",
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)
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)
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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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