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233 lines
8.4 KiB
Python
233 lines
8.4 KiB
Python
# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
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#
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# SPDX-License-Identifier: Apache-2.0
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import logging
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import pytest
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from haystack import Document
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from haystack.dataclasses import ByteStream, SparseEmbedding
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from haystack.components.preprocessors import DocumentCleaner
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class TestDocumentCleaner:
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def test_init(self):
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cleaner = DocumentCleaner()
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assert cleaner.remove_empty_lines is True
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assert cleaner.remove_extra_whitespaces is True
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assert cleaner.remove_repeated_substrings is False
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assert cleaner.remove_substrings is None
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assert cleaner.remove_regex is None
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assert cleaner.keep_id is False
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def test_non_text_document(self, caplog):
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with caplog.at_level(logging.WARNING):
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cleaner = DocumentCleaner()
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cleaner.run(documents=[Document()])
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assert "DocumentCleaner only cleans text documents but document.content for document ID" in caplog.text
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def test_single_document(self):
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with pytest.raises(TypeError, match="DocumentCleaner expects a List of Documents as input."):
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cleaner = DocumentCleaner()
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cleaner.run(documents=Document())
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def test_empty_list(self):
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cleaner = DocumentCleaner()
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result = cleaner.run(documents=[])
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assert result == {"documents": []}
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def test_remove_empty_lines(self):
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cleaner = DocumentCleaner(remove_extra_whitespaces=False)
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result = cleaner.run(
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documents=[
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Document(
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content="This is a text with some words. \f"
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""
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"There is a second sentence. "
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""
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"And there is a third sentence."
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)
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]
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)
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assert len(result["documents"]) == 1
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assert (
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result["documents"][0].content
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== "This is a text with some words. \fThere is a second sentence. And there is a third sentence."
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)
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def test_remove_whitespaces(self):
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cleaner = DocumentCleaner(remove_empty_lines=False)
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result = cleaner.run(
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documents=[
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Document(
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content=" This is a text with some words. "
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""
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"There is a second sentence. "
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""
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"And there is a third sentence.\f "
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)
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]
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)
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assert len(result["documents"]) == 1
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assert result["documents"][0].content == (
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"This is a text with some words. There is a second sentence. And there is a third sentence.\f"
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)
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def test_remove_substrings(self):
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cleaner = DocumentCleaner(remove_substrings=["This", "A", "words", "🪲"])
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result = cleaner.run(documents=[Document(content="This is a text with some words.\f🪲")])
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assert len(result["documents"]) == 1
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assert result["documents"][0].content == " is a text with some .\f"
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def test_remove_regex(self):
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cleaner = DocumentCleaner(remove_regex=r"\s\s+")
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result = cleaner.run(documents=[Document(content="This is a text \f with some words.")])
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assert len(result["documents"]) == 1
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assert result["documents"][0].content == "This is a text\fwith some words."
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def test_remove_repeated_substrings(self):
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cleaner = DocumentCleaner(
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remove_empty_lines=False, remove_extra_whitespaces=False, remove_repeated_substrings=True
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)
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text = """First Page\f This is a header.
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Page of
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2
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4
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Lorem ipsum dolor sit amet
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This is a footer number 1
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This is footer number 2 This is a header.
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Page of
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3
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4
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Sid ut perspiciatis unde
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This is a footer number 1
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This is footer number 2 This is a header.
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Page of
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4
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4
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Sed do eiusmod tempor.
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This is a footer number 1
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This is footer number 2"""
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expected_text = """First Page\f 2
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4
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Lorem ipsum dolor sit amet 3
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4
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Sid ut perspiciatis unde 4
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4
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Sed do eiusmod tempor."""
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result = cleaner.run(documents=[Document(content=text)])
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assert result["documents"][0].content == expected_text
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def test_copy_metadata(self):
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cleaner = DocumentCleaner()
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documents = [
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Document(content="Text. ", meta={"name": "doc 0"}),
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Document(content="Text. ", meta={"name": "doc 1"}),
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]
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result = cleaner.run(documents=documents)
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assert len(result["documents"]) == 2
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assert result["documents"][0].id != result["documents"][1].id
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for doc, cleaned_doc in zip(documents, result["documents"]):
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assert doc.meta == cleaned_doc.meta
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assert cleaned_doc.content == "Text."
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def test_keep_id_does_not_alter_document_ids(self):
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cleaner = DocumentCleaner(keep_id=True)
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documents = [Document(content="Text. ", id="1"), Document(content="Text. ", id="2")]
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result = cleaner.run(documents=documents)
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assert len(result["documents"]) == 2
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assert result["documents"][0].id == "1"
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assert result["documents"][1].id == "2"
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def test_unicode_normalization(self):
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text = """\
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ｱｲｳｴｵ
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Comment ça va
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مرحبا بالعالم
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em Space"""
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expected_text_NFC = """\
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ｱｲｳｴｵ
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Comment ça va
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مرحبا بالعالم
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em Space"""
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expected_text_NFD = """\
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ｱｲｳｴｵ
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Comment ça va
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مرحبا بالعالم
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em Space"""
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expected_text_NFKC = """\
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アイウエオ
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Comment ça va
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مرحبا بالعالم
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em Space"""
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expected_text_NFKD = """\
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アイウエオ
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Comment ça va
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مرحبا بالعالم
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em Space"""
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nfc_cleaner = DocumentCleaner(unicode_normalization="NFC", remove_extra_whitespaces=False)
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nfd_cleaner = DocumentCleaner(unicode_normalization="NFD", remove_extra_whitespaces=False)
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nfkc_cleaner = DocumentCleaner(unicode_normalization="NFKC", remove_extra_whitespaces=False)
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nfkd_cleaner = DocumentCleaner(unicode_normalization="NFKD", remove_extra_whitespaces=False)
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nfc_result = nfc_cleaner.run(documents=[Document(content=text)])
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nfd_result = nfd_cleaner.run(documents=[Document(content=text)])
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nfkc_result = nfkc_cleaner.run(documents=[Document(content=text)])
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nfkd_result = nfkd_cleaner.run(documents=[Document(content=text)])
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assert nfc_result["documents"][0].content == expected_text_NFC
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assert nfd_result["documents"][0].content == expected_text_NFD
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assert nfkc_result["documents"][0].content == expected_text_NFKC
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assert nfkd_result["documents"][0].content == expected_text_NFKD
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def test_ascii_only(self):
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text = """\
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ｱｲｳｴｵ
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Comment ça va
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Á
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مرحبا بالعالم
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em Space"""
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expected_text = """\
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\n\
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Comment ca va
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A
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\n\
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em Space"""
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cleaner = DocumentCleaner(ascii_only=True, remove_extra_whitespaces=False, remove_empty_lines=False)
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result = cleaner.run(documents=[Document(content=text)])
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assert result["documents"][0].content == expected_text
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def test_other_document_fields_are_not_lost(self):
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cleaner = DocumentCleaner(keep_id=True)
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document = Document(
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content="This is a text with some words. \nThere is a second sentence. \nAnd there is a third sentence.\n",
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blob=ByteStream.from_string("some_data"),
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meta={"data": 1},
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score=0.1,
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embedding=[0.1, 0.2, 0.3],
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sparse_embedding=SparseEmbedding([0, 2], [0.1, 0.3]),
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)
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res = cleaner.run(documents=[document])
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assert len(res) == 1
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assert len(res["documents"])
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assert res["documents"][0].id == document.id
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assert res["documents"][0].content == (
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"This is a text with some words. There is a second sentence. And there is a third sentence."
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)
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assert res["documents"][0].blob == document.blob
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assert res["documents"][0].meta == document.meta
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assert res["documents"][0].score == document.score
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assert res["documents"][0].embedding == document.embedding
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assert res["documents"][0].sparse_embedding == document.sparse_embedding
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