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"""Tests for dataset.select_rows(searches=[...])."""
from typing import Iterable, cast
import numpy as np
import pytest
from pytest import approx
from pytest_mock import MockerFixture
from sklearn.preprocessing import normalize
from typing_extensions import override
from ..concepts.concept import ExampleIn, LogisticEmbeddingModel
from ..concepts.db_concept import ConceptUpdate, DiskConceptDB
from ..db_manager import set_default_dataset_cls
from ..schema import UUID_COLUMN, Item, RichData, SignalInputType
from ..signals.concept_scorer import ConceptScoreSignal
from ..signals.semantic_similarity import SemanticSimilaritySignal
from ..signals.signal import TextEmbeddingSignal, clear_signal_registry, register_signal
from ..signals.substring_search import SubstringSignal
from .dataset import ConceptQuery, KeywordQuery, ListOp, Search, SemanticQuery, SortOrder
from .dataset_duckdb import DatasetDuckDB
from .dataset_test_utils import TestDataMaker, enriched_embedding_span, enriched_item
from .dataset_utils import lilac_embedding, lilac_span
TEST_DATA: list[Item] = [{
UUID_COLUMN: '1',
'text': 'hello world',
'text2': 'again hello world',
}, {
UUID_COLUMN: '2',
'text': 'looking for world in text',
'text2': 'again looking for world in text',
}, {
UUID_COLUMN: '3',
'text': 'unrelated text',
'text2': 'again unrelated text'
}]
EMBEDDINGS: list[tuple[str, list[float]]] = [
('hello.', [1.0, 0.0, 0.0]),
('hello2.', [1.0, 1.0, 0.0]),
('hello world.', [1.0, 1.0, 1.0]),
('hello world2.', [2.0, 1.0, 1.0]),
('random negative 1', [0, 0, 0.3]),
('random negative 2', [0, 0, 0.4]),
('random negative 3', [0, 0.1, 0.5]),
('random negative 4', [0.1, 0, 0.4]),
]
STR_EMBEDDINGS: dict[str, list[float]] = {text: embedding for text, embedding in EMBEDDINGS}
@pytest.fixture(scope='module', autouse=True)
def setup_teardown() -> Iterable[None]:
# Setup.
set_default_dataset_cls(DatasetDuckDB)
register_signal(TestEmbedding)
# Unit test runs.
yield
# Teardown.
clear_signal_registry()
def test_search_keyword(make_test_data: TestDataMaker) -> None:
dataset = make_test_data(TEST_DATA)
query = 'world'
result = dataset.select_rows(
searches=[Search(path='text', query=KeywordQuery(type='keyword', search=query))],
combine_columns=True)
expected_signal_udf = SubstringSignal(query=query)
assert list(result) == [{
UUID_COLUMN: '1',
'text': enriched_item('hello world', {expected_signal_udf.key(): [lilac_span(6, 11)]}),
'text2': 'again hello world'
}, {
UUID_COLUMN: '2',
'text': enriched_item('looking for world in text',
{expected_signal_udf.key(): [lilac_span(12, 17)]}),
'text2': 'again looking for world in text',
}]
def test_search_keyword_special_chars(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': 'This is 100%',
}, {
UUID_COLUMN: '2',
'text': 'This has _underscore_',
}])
query = '100%'
result = dataset.select_rows(
searches=[Search(path='text', query=KeywordQuery(type='keyword', search=query))],
combine_columns=True)
expected_signal_udf = SubstringSignal(query=query)
assert list(result) == [{
UUID_COLUMN: '1',
'text': enriched_item('This is 100%', {expected_signal_udf.key(): [lilac_span(8, 12)]}),
}]
query = '_underscore_'
result = dataset.select_rows(
searches=[Search(path='text', query=KeywordQuery(type='keyword', search=query))],
combine_columns=True)
expected_signal_udf = SubstringSignal(query=query)
assert list(result) == [{
UUID_COLUMN: '2',
'text': enriched_item('This has _underscore_',
{expected_signal_udf.key(): [lilac_span(9, 21)]}),
}]
def test_search_keyword_multiple(make_test_data: TestDataMaker) -> None:
dataset = make_test_data(TEST_DATA)
query_world = 'world'
query_looking_world = 'looking for world'
expected_world_udf = SubstringSignal(query=query_world)
expected_again_looking_udf = SubstringSignal(query=query_looking_world)
result = dataset.select_rows(
searches=[
Search(path='text', query=KeywordQuery(type='keyword', search=query_world)),
Search(path='text2', query=KeywordQuery(type='keyword', search=query_looking_world)),
],
combine_columns=True)
assert list(result) == [{
UUID_COLUMN: '2',
'text': enriched_item('looking for world in text', {
expected_world_udf.key(): [lilac_span(12, 17)],
}),
'text2': enriched_item('again looking for world in text',
{expected_again_looking_udf.key(): [lilac_span(6, 23)]})
}]
def test_search_keyword_with_filters(make_test_data: TestDataMaker) -> None:
dataset = make_test_data(TEST_DATA)
query = 'world'
result = dataset.select_rows(
filters=[(UUID_COLUMN, ListOp.IN, ['1', '3'])],
searches=[Search(path='text', query=KeywordQuery(type='keyword', search=query))],
combine_columns=True)
expected_signal_udf = SubstringSignal(query=query)
assert list(result) == [
{
UUID_COLUMN: '1',
'text': enriched_item('hello world', {expected_signal_udf.key(): [lilac_span(6, 11)]}),
'text2': 'again hello world'
},
# The second row doesn't match the UUID filter.
]
class TestEmbedding(TextEmbeddingSignal):
"""A test embed function."""
name = 'test_embedding'
@override
def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
"""Call the embedding function."""
for example in data:
embedding = np.array(STR_EMBEDDINGS[cast(str, example)])
embedding = normalize([embedding])[0]
yield [lilac_embedding(0, len(example), embedding)]
def test_semantic_search(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': 'hello world.',
}, {
UUID_COLUMN: '2',
'text': 'hello world2.',
}])
test_embedding = TestEmbedding()
dataset.compute_signal(test_embedding, ('text'))
query = 'hello2.'
result = dataset.select_rows(
searches=[
Search(
path='text', query=SemanticQuery(type='semantic', search=query, embedding='test_embedding'))
],
combine_columns=True)
expected_signal_udf = SemanticSimilaritySignal(query=query, embedding='test_embedding')
assert list(result) == [
# Results are sorted by score desc.
{
UUID_COLUMN: '2',
'text': enriched_item(
'hello world2.', {
test_embedding.key():
[enriched_embedding_span(0, 13, {expected_signal_udf.key(): approx(0.916, 1e-3)})]
})
},
{
UUID_COLUMN: '1',
'text': enriched_item(
'hello world.', {
test_embedding.key():
[enriched_embedding_span(0, 12, {expected_signal_udf.key(): approx(0.885, 1e-3)})]
})
},
]
def test_concept_search(make_test_data: TestDataMaker, mocker: MockerFixture) -> None:
concept_model_mock = mocker.spy(LogisticEmbeddingModel, 'fit')
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': 'hello world.',
}, {
UUID_COLUMN: '2',
'text': 'hello world2.',
}, {
UUID_COLUMN: '3',
'text': 'random negative 1',
}, {
UUID_COLUMN: '4',
'text': 'random negative 2',
}, {
UUID_COLUMN: '5',
'text': 'random negative 3',
}, {
UUID_COLUMN: '6',
'text': 'random negative 4',
}])
test_embedding = TestEmbedding()
dataset.compute_signal(test_embedding, ('text'))
concept_db = DiskConceptDB()
concept_db.create(namespace='test_namespace', name='test_concept', type=SignalInputType.TEXT)
concept_db.edit(
'test_namespace', 'test_concept',
ConceptUpdate(insert=[
ExampleIn(label=False, text='hello world.'),
ExampleIn(label=True, text='hello world2.')
]))
result = dataset.select_rows(
searches=[
Search(
path='text',
query=ConceptQuery(
type='concept',
concept_namespace='test_namespace',
concept_name='test_concept',
embedding='test_embedding'))
],
filters=[(UUID_COLUMN, ListOp.IN, ['1', '2'])],
combine_columns=True)
expected_signal_udf = ConceptScoreSignal(
namespace='test_namespace', concept_name='test_concept', embedding='test_embedding')
assert list(result) == [
# Results are sorted by score desc.
{
UUID_COLUMN: '2',
'text': enriched_item(
'hello world2.', {
test_embedding.key():
[enriched_embedding_span(0, 13, {expected_signal_udf.key(): approx(0.75, abs=0.25)})],
'test_namespace/test_concept/labels': [lilac_span(0, 13, {'label': True})]
})
},
{
UUID_COLUMN: '1',
'text': enriched_item(
'hello world.', {
test_embedding.key():
[enriched_embedding_span(0, 12, {expected_signal_udf.key(): approx(0.25, abs=0.25)})],
'test_namespace/test_concept/labels': [lilac_span(0, 12, {'label': False})]
})
},
]
# Make sure fit was called with negative examples.
(_, embeddings, labels, _) = concept_model_mock.call_args_list[-1].args
assert embeddings.shape == (8, 3)
assert labels == [
# Negative implicit labels.
False,
False,
False,
False,
False,
False,
# Explicit labels.
False,
True
]
def test_sort_override_search(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': 'hello world.',
'value': 10
}, {
UUID_COLUMN: '2',
'text': 'hello world2.',
'value': 20
}])
test_embedding = TestEmbedding()
dataset.compute_signal(test_embedding, ('text'))
query = 'hello2.'
search = Search(
path='text', query=SemanticQuery(type='semantic', search=query, embedding='test_embedding'))
expected_signal_udf = SemanticSimilaritySignal(query=query, embedding='test_embedding')
expected_item_1 = {
UUID_COLUMN: '1',
'text': enriched_item(
'hello world.', {
test_embedding.key():
[enriched_embedding_span(0, 12, {expected_signal_udf.key(): approx(0.885, 1e-3)})]
}),
'value': 10
}
expected_item_2 = {
UUID_COLUMN: '2',
'text': enriched_item(
'hello world2.', {
test_embedding.key():
[enriched_embedding_span(0, 13, {expected_signal_udf.key(): approx(0.916, 1e-3)})]
}),
'value': 20
}
sort_order = SortOrder.ASC
result = dataset.select_rows(
searches=[search], sort_by=[('value',)], sort_order=sort_order, combine_columns=True)
assert list(result) == [
# Results are sorted by score ascending.
expected_item_1,
expected_item_2
]
sort_order = SortOrder.DESC
result = dataset.select_rows(
searches=[search], sort_by=[('text',)], sort_order=sort_order, combine_columns=True)
assert list(result) == [
# Results are sorted by score descending.
expected_item_2,
expected_item_1
]
def test_search_keyword_and_semantic(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': 'hello world.',
}, {
UUID_COLUMN: '2',
'text': 'hello world2.',
}])
test_embedding = TestEmbedding()
dataset.compute_signal(test_embedding, ('text'))
query = 'hello2.'
keyword_query = 'rld2'
result = dataset.select_rows(
searches=[
Search(
path='text', query=SemanticQuery(type='semantic', search=query,
embedding='test_embedding')),
Search(path='text', query=KeywordQuery(type='keyword', search=keyword_query))
],
combine_columns=True)
expected_semantic_signal = SemanticSimilaritySignal(query=query, embedding='test_embedding')
expected_keyword_signal = SubstringSignal(query=keyword_query)
assert list(result) == [
# Results are sorted by score desc.
{
UUID_COLUMN: '2',
'text': enriched_item(
'hello world2.', {
test_embedding.key():
[enriched_embedding_span(0, 13, {expected_semantic_signal.key(): approx(0.916, 1e-3)})],
expected_keyword_signal.key(): [lilac_span(8, 12)],
})
},
# UUID '1' is not returned because it does not match the keyword query.
]
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