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If the input text is None, Tantivy raises an error complaining it cannot add a NoneType. We handle this upstream so None's are not added to the document. If all of the indexed fields are None then we skip this document.
165 lines
4.7 KiB
Python
165 lines
4.7 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 os
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import random
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from unittest import mock
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import numpy as np
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import pandas as pd
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import pytest
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import tantivy
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import lancedb as ldb
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import lancedb.fts
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@pytest.fixture
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def table(tmp_path) -> ldb.table.LanceTable:
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db = ldb.connect(tmp_path)
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vectors = [np.random.randn(128) for _ in range(100)]
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nouns = ("puppy", "car", "rabbit", "girl", "monkey")
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verbs = ("runs", "hits", "jumps", "drives", "barfs")
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adv = ("crazily.", "dutifully.", "foolishly.", "merrily.", "occasionally.")
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adj = ("adorable", "clueless", "dirty", "odd", "stupid")
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text = [
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" ".join(
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[
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nouns[random.randrange(0, 5)],
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verbs[random.randrange(0, 5)],
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adv[random.randrange(0, 5)],
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adj[random.randrange(0, 5)],
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]
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)
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for _ in range(100)
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]
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table = db.create_table(
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"test",
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data=pd.DataFrame(
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{
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"vector": vectors,
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"id": [i % 2 for i in range(100)],
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"text": text,
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"text2": text,
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"nested": [{"text": t} for t in text],
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}
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),
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)
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return table
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def test_create_index(tmp_path):
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index = ldb.fts.create_index(str(tmp_path / "index"), ["text"])
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assert isinstance(index, tantivy.Index)
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assert os.path.exists(str(tmp_path / "index"))
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def test_populate_index(tmp_path, table):
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index = ldb.fts.create_index(str(tmp_path / "index"), ["text"])
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assert ldb.fts.populate_index(index, table, ["text"]) == len(table)
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def test_search_index(tmp_path, table):
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index = ldb.fts.create_index(str(tmp_path / "index"), ["text"])
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ldb.fts.populate_index(index, table, ["text"])
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index.reload()
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results = ldb.fts.search_index(index, query="puppy", limit=10)
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assert len(results) == 2
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assert len(results[0]) == 10 # row_ids
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assert len(results[1]) == 10 # _distance
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def test_create_index_from_table(tmp_path, table):
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table.create_fts_index("text")
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df = table.search("puppy").limit(10).select(["text"]).to_pandas()
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assert len(df) == 10
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assert "text" in df.columns
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# Check whether it can be updated
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table.add(
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[
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{
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"vector": np.random.randn(128),
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"id": 101,
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"text": "gorilla",
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"text2": "gorilla",
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"nested": {"text": "gorilla"},
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}
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]
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)
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with pytest.raises(ValueError, match="already exists"):
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table.create_fts_index("text")
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table.create_fts_index("text", replace=True)
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assert len(table.search("gorilla").limit(1).to_pandas()) == 1
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def test_create_index_multiple_columns(tmp_path, table):
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table.create_fts_index(["text", "text2"])
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df = table.search("puppy").limit(10).to_pandas()
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assert len(df) == 10
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assert "text" in df.columns
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assert "text2" in df.columns
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def test_empty_rs(tmp_path, table, mocker):
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table.create_fts_index(["text", "text2"])
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mocker.patch("lancedb.fts.search_index", return_value=([], []))
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df = table.search("puppy").limit(10).to_pandas()
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assert len(df) == 0
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def test_nested_schema(tmp_path, table):
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table.create_fts_index("nested.text")
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rs = table.search("puppy").limit(10).to_list()
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assert len(rs) == 10
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def test_search_index_with_filter(table):
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table.create_fts_index("text")
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orig_import = __import__
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def import_mock(name, *args):
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if name == "duckdb":
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raise ImportError
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return orig_import(name, *args)
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# no duckdb
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with mock.patch("builtins.__import__", side_effect=import_mock):
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rs = table.search("puppy").where("id=1").limit(10).to_list()
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for r in rs:
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assert r["id"] == 1
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# yes duckdb
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rs2 = table.search("puppy").where("id=1").limit(10).to_list()
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for r in rs2:
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assert r["id"] == 1
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assert rs == rs2
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def test_null_input(table):
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table.add(
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[
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{
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"vector": np.random.randn(128),
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"id": 101,
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"text": None,
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"text2": None,
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"nested": {"text": None},
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}
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]
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)
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table.create_fts_index("text")
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