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3c46d7f268
Sometimes LangChain would insert a single `[np.nan]` as a placeholder if the embedding function failed. This causes a problem for Lance format because then the array can't be stored as a FixedSizedListArray. Instead: 1. By default we remove rows with embedding lengths less than the maximum length in the batch 2. If `strict=True` kwargs is set to True, then a `ValueError` is raised if the embeddings aren't all the same length --------- Co-authored-by: Chang She <chang@lancedb.com>
116 lines
3.3 KiB
Python
116 lines
3.3 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.query import LanceQueryBuilder
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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("/tmp/lance/")
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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 = 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_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.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 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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table._conn = mock.MagicMock()
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table._conn.is_managed_remote = False
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builder.to_arrow()
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ds.to_table.assert_called_once_with(
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columns=["b"],
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filter="b < 10",
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nearest={
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"column": vector_column_name,
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"q": query,
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"k": 2,
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"metric": "cosine",
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"nprobes": 20,
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"refine_factor": None,
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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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