mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-07-29 11:50:34 +00:00
93312138de
* first attempt to add frontmatter of markdown to the metadata * remove bug fix * running black and pre-commit * moving the import line * adding a test * adding pydoc * fix to removing code blocks in markdown converter * adding a test * fixing a test * improving tests * adding language to code block
409 lines
16 KiB
Python
409 lines
16 KiB
Python
from typing import List
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import os
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import sys
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from pathlib import Path
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import subprocess
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import csv
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import pytest
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from haystack import Document
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from haystack.nodes import (
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MarkdownConverter,
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DocxToTextConverter,
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PDFToTextConverter,
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PDFToTextOCRConverter,
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TikaConverter,
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AzureConverter,
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ParsrConverter,
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TextConverter,
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CsvTextConverter,
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)
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from ..conftest import SAMPLES_PATH
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@pytest.mark.tika
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@pytest.mark.parametrize("Converter", [PDFToTextConverter, TikaConverter, PDFToTextOCRConverter])
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def test_convert(Converter):
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converter = Converter()
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document = converter.run(file_paths=SAMPLES_PATH / "pdf" / "sample_pdf_1.pdf")[0]["documents"][0]
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pages = document.content.split("\f")
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assert (
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len(pages) != 1 and pages[0] != ""
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), f'{type(converter).__name__} did return a single empty page indicating a potential issue with your installed poppler version. Try installing via "conda install -c conda-forge poppler" and check test_pdftoppm_command_format()'
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assert len(pages) == 4 # the sample PDF file has four pages.
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assert pages[0] != "" # the page 1 of PDF contains text.
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assert pages[2] == "" # the page 3 of PDF file is empty.
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# assert text is retained from the document.
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# As whitespace can differ (\n," ", etc.), we standardize all to simple whitespace
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page_standard_whitespace = " ".join(pages[0].split())
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assert "Adobe Systems made the PDF specification available free of charge in 1993." in page_standard_whitespace
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# Marked as integration because it uses poppler, which is not installed in the unit tests suite
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@pytest.mark.integration
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@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Poppler not installed on Windows CI")
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def test_pdftoppm_command_format():
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# Haystack's PDFToTextOCRConverter uses pdf2image, which calls pdftoppm internally.
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# Some installations of pdftoppm are incompatible with Haystack and won't raise an error but just return empty converted documents
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# This test runs pdftoppm directly to check whether pdftoppm accepts the command format that pdf2image uses in Haystack
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proc = subprocess.Popen(
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["pdftoppm", f"{SAMPLES_PATH}/pdf/sample_pdf_1.pdf"], stdout=subprocess.PIPE, stderr=subprocess.PIPE
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)
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out, err = proc.communicate()
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# If usage info of pdftoppm is sent to stderr then it's because Haystack's pdf2image uses an incompatible command format
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assert (
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not err
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), 'Your installation of poppler is incompatible with Haystack. Try installing via "conda install -c conda-forge poppler"'
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@pytest.mark.parametrize("Converter", [PDFToTextConverter])
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def test_pdf_command_whitespaces(Converter):
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converter = Converter()
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document = converter.run(file_paths=SAMPLES_PATH / "pdf" / "sample pdf file with spaces on file name.pdf")[0][
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"documents"
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][0]
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assert "ɪ" in document.content
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@pytest.mark.parametrize("Converter", [PDFToTextConverter])
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def test_pdf_encoding(Converter):
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converter = Converter()
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document = converter.run(file_paths=SAMPLES_PATH / "pdf" / "sample_pdf_2.pdf")[0]["documents"][0]
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assert "ɪ" in document.content
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document = converter.run(file_paths=SAMPLES_PATH / "pdf" / "sample_pdf_2.pdf", encoding="Latin1")[0]["documents"][0]
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assert "ɪ" not in document.content
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@pytest.mark.parametrize("Converter", [PDFToTextConverter])
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def test_pdf_layout(Converter):
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converter = Converter(keep_physical_layout=True)
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document = converter.convert(file_path=SAMPLES_PATH / "pdf" / "sample_pdf_3.pdf")[0]
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assert str(document.content).startswith("This is the second test sentence.")
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@pytest.mark.parametrize("Converter", [PDFToTextConverter])
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def test_pdf_ligatures(Converter):
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converter = Converter()
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document = converter.run(file_paths=SAMPLES_PATH / "pdf" / "sample_pdf_2.pdf")[0]["documents"][0]
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assert "ff" not in document.content
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assert "ɪ" in document.content
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document = converter.run(file_paths=SAMPLES_PATH / "pdf" / "sample_pdf_2.pdf", known_ligatures={})[0]["documents"][
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0
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]
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assert "ff" in document.content
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assert "ɪ" in document.content
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document = converter.run(file_paths=SAMPLES_PATH / "pdf" / "sample_pdf_2.pdf", known_ligatures={"ɪ": "i"})[0][
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"documents"
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][0]
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assert "ff" in document.content
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assert "ɪ" not in document.content
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@pytest.mark.tika
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@pytest.mark.parametrize("Converter", [PDFToTextConverter, TikaConverter])
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def test_table_removal(Converter):
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converter = Converter(remove_numeric_tables=True)
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document = converter.convert(file_path=SAMPLES_PATH / "pdf" / "sample_pdf_1.pdf")[0]
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pages = document.content.split("\f")
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# assert numeric rows are removed from the table.
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assert "324" not in pages[0]
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assert "54x growth" not in pages[0]
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@pytest.mark.tika
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@pytest.mark.parametrize("Converter", [PDFToTextConverter, TikaConverter])
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def test_language_validation(Converter, caplog):
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converter = Converter(valid_languages=["en"])
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converter.convert(file_path=SAMPLES_PATH / "pdf" / "sample_pdf_1.pdf")
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assert "sample_pdf_1.pdf is not one of ['en']." not in caplog.text
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converter = Converter(valid_languages=["de"])
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converter.convert(file_path=SAMPLES_PATH / "pdf" / "sample_pdf_1.pdf")
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assert "sample_pdf_1.pdf is not one of ['de']." in caplog.text
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def test_docx_converter():
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converter = DocxToTextConverter()
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document = converter.convert(file_path=SAMPLES_PATH / "docx" / "sample_docx.docx")[0]
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assert document.content.startswith("Sample Docx File")
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def test_markdown_converter():
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converter = MarkdownConverter()
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document = converter.convert(file_path=SAMPLES_PATH / "markdown" / "sample.md")[0]
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assert document.content.startswith("\nWhat to build with Haystack")
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assert "# git clone https://github.com/deepset-ai/haystack.git" not in document.content
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def test_markdown_converter_headline_extraction():
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expected_headlines = [
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("What to build with Haystack", 1),
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("Core Features", 1),
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("Quick Demo", 1),
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("2nd level headline for testing purposes", 2),
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("3rd level headline for testing purposes", 3),
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]
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converter = MarkdownConverter(extract_headlines=True, remove_code_snippets=False)
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document = converter.convert(file_path=SAMPLES_PATH / "markdown" / "sample.md")[0]
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# Check if correct number of headlines are extracted
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assert len(document.meta["headlines"]) == 5
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for extracted_headline, (expected_headline, expected_level) in zip(document.meta["headlines"], expected_headlines):
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# Check if correct headline and level is extracted
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assert extracted_headline["headline"] == expected_headline
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assert extracted_headline["level"] == expected_level
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# Check if correct start_idx is extracted
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start_idx = extracted_headline["start_idx"]
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hl_len = len(extracted_headline["headline"])
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assert extracted_headline["headline"] == document.content[start_idx : start_idx + hl_len]
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def test_markdown_converter_frontmatter_to_meta():
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converter = MarkdownConverter(add_frontmatter_to_meta=True)
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document = converter.convert(file_path=SAMPLES_PATH / "markdown" / "sample.md")[0]
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assert document.meta["type"] == "intro"
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assert document.meta["date"] == "1.1.2023"
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def test_markdown_converter_remove_code_snippets():
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converter = MarkdownConverter(remove_code_snippets=False)
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document = converter.convert(file_path=SAMPLES_PATH / "markdown" / "sample.md")[0]
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assert document.content.startswith("pip install farm-haystack")
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def test_azure_converter():
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# Check if Form Recognizer endpoint and credential key in environment variables
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if "AZURE_FORMRECOGNIZER_ENDPOINT" in os.environ and "AZURE_FORMRECOGNIZER_KEY" in os.environ:
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converter = AzureConverter(
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endpoint=os.environ["AZURE_FORMRECOGNIZER_ENDPOINT"],
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credential_key=os.environ["AZURE_FORMRECOGNIZER_KEY"],
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save_json=True,
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)
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docs = converter.convert(file_path=SAMPLES_PATH / "pdf" / "sample_pdf_1.pdf")
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assert len(docs) == 2
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assert docs[0].content_type == "table"
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assert docs[0].content.shape[0] == 4 # number of rows
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assert docs[0].content.shape[1] == 5 # number of columns, Form Recognizer assumes there are 5 columns
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assert list(docs[0].content.columns) == ["", "Column 1", "", "Column 2", "Column 3"]
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assert list(docs[0].content.iloc[3]) == ["D", "$54.35", "", "$6345.", ""]
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assert (
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docs[0].meta["preceding_context"] == "specification. These proprietary technologies are not "
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"standardized and their\nspecification is published only on "
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"Adobe's website. Many of them are also not\nsupported by "
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"popular third-party implementations of PDF."
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)
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assert docs[0].meta["following_context"] == ""
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assert docs[0].meta["page"] == 1
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assert docs[1].content_type == "text"
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assert docs[1].content.startswith("A sample PDF file")
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@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Parsr not running on Windows CI")
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def test_parsr_converter():
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converter = ParsrConverter()
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docs = converter.convert(file_path=str((SAMPLES_PATH / "pdf" / "sample_pdf_1.pdf").absolute()))
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assert len(docs) == 2
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assert docs[0].content_type == "table"
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assert docs[0].content.shape[0] == 4 # number of rows
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assert docs[0].content.shape[1] == 4
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assert list(docs[0].content.columns) == ["", "Column 1", "Column 2", "Column 3"]
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assert list(docs[0].content.iloc[3]) == ["D", "$54.35", "$6345.", ""]
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assert (
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docs[0].meta["preceding_context"] == "specification. These proprietary technologies are not "
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"standardized and their\nspecification is published only on "
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"Adobe's website. Many of them are also not\nsupported by popular "
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"third-party implementations of PDF."
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)
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assert docs[0].meta["following_context"] == ""
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assert docs[0].meta["page"] == 1
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assert docs[1].content_type == "text"
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assert docs[1].content.startswith("A sample PDF file")
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assert docs[1].content.endswith("Page 4 of Sample PDF\n… the page 3 is empty.")
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@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Parsr not running on Windows CI")
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def test_parsr_converter_headline_extraction():
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expected_headlines = [
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[("Lorem ipsum", 1), ("Cras fringilla ipsum magna, in fringilla dui commodo\na.", 2)],
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[
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("Lorem ipsum", 1),
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("Lorem ipsum dolor sit amet, consectetur adipiscing\nelit. Nunc ac faucibus odio.", 2),
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("Cras fringilla ipsum magna, in fringilla dui commodo\na.", 2),
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("Lorem ipsum dolor sit amet, consectetur adipiscing\nelit.", 2),
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("Maecenas mauris lectus, lobortis et purus mattis, blandit\ndictum tellus.", 2),
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("In eleifend velit vitae libero sollicitudin euismod.", 2),
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],
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]
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converter = ParsrConverter()
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docs = converter.convert(file_path=str((SAMPLES_PATH / "pdf" / "sample_pdf_4.pdf").absolute()))
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assert len(docs) == 2
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for doc, expectation in zip(docs, expected_headlines):
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for extracted_headline, (expected_headline, expected_level) in zip(doc.meta["headlines"], expectation):
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# Check if correct headline and level is extracted
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assert extracted_headline["headline"] == expected_headline
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assert extracted_headline["level"] == expected_level
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# Check if correct start_idx is extracted
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if doc.content_type == "text":
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start_idx = extracted_headline["start_idx"]
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hl_len = len(extracted_headline["headline"])
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assert extracted_headline["headline"] == doc.content[start_idx : start_idx + hl_len]
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def test_id_hash_keys_from_pipeline_params():
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doc_path = SAMPLES_PATH / "docs" / "doc_1.txt"
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meta_1 = {"key": "a"}
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meta_2 = {"key": "b"}
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meta = [meta_1, meta_2]
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converter = TextConverter()
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output, _ = converter.run(file_paths=[doc_path, doc_path], meta=meta, id_hash_keys=["content", "meta"])
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documents = output["documents"]
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unique_ids = set(d.id for d in documents)
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assert len(documents) == 2
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assert len(unique_ids) == 2
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def write_as_csv(data: List[List[str]], file_path: Path):
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with open(file_path, "w") as f:
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writer = csv.writer(f)
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writer.writerows(data)
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@pytest.mark.integration
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def test_csv_to_document_with_qa_headers(tmp_path):
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node = CsvTextConverter()
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csv_path = tmp_path / "csv_qa_with_headers.csv"
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rows = [
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["question", "answer"],
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["What is Haystack ?", "Haystack is an NLP Framework to use transformers in your Applications."],
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]
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write_as_csv(rows, csv_path)
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output, edge = node.run(file_paths=csv_path)
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assert edge == "output_1"
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assert "documents" in output
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assert len(output["documents"]) == 1
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doc = output["documents"][0]
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assert isinstance(doc, Document)
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assert doc.content == "What is Haystack ?"
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assert doc.meta["answer"] == "Haystack is an NLP Framework to use transformers in your Applications."
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@pytest.mark.integration
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def test_csv_to_document_with_wrong_qa_headers(tmp_path):
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node = CsvTextConverter()
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csv_path = tmp_path / "csv_qa_with_wrong_headers.csv"
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rows = [
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["wrong", "headers"],
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["What is Haystack ?", "Haystack is an NLP Framework to use transformers in your Applications."],
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]
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write_as_csv(rows, csv_path)
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with pytest.raises(ValueError, match="The CSV must contain two columns named 'question' and 'answer'"):
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node.run(file_paths=csv_path)
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@pytest.mark.integration
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def test_csv_to_document_with_one_wrong_qa_headers(tmp_path):
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node = CsvTextConverter()
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csv_path = tmp_path / "csv_qa_with_wrong_headers.csv"
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rows = [
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["wrong", "answers"],
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["What is Haystack ?", "Haystack is an NLP Framework to use transformers in your Applications."],
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]
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write_as_csv(rows, csv_path)
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with pytest.raises(ValueError, match="The CSV must contain two columns named 'question' and 'answer'"):
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node.run(file_paths=csv_path)
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@pytest.mark.integration
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def test_csv_to_document_with_another_wrong_qa_headers(tmp_path):
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node = CsvTextConverter()
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csv_path = tmp_path / "csv_qa_with_wrong_headers.csv"
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rows = [
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["question", "wrong"],
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["What is Haystack ?", "Haystack is an NLP Framework to use transformers in your Applications."],
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]
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write_as_csv(rows, csv_path)
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with pytest.raises(ValueError, match="The CSV must contain two columns named 'question' and 'answer'"):
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node.run(file_paths=csv_path)
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@pytest.mark.integration
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def test_csv_to_document_with_one_column(tmp_path):
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node = CsvTextConverter()
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csv_path = tmp_path / "csv_qa_with_wrong_headers.csv"
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rows = [["question"], ["What is Haystack ?"]]
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write_as_csv(rows, csv_path)
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with pytest.raises(ValueError, match="The CSV must contain two columns named 'question' and 'answer'"):
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node.run(file_paths=csv_path)
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@pytest.mark.integration
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def test_csv_to_document_with_three_columns(tmp_path):
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node = CsvTextConverter()
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csv_path = tmp_path / "csv_qa_with_wrong_headers.csv"
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rows = [
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["question", "answer", "notes"],
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["What is Haystack ?", "Haystack is an NLP Framework to use transformers in your Applications.", "verified"],
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]
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write_as_csv(rows, csv_path)
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with pytest.raises(ValueError, match="The CSV must contain two columns named 'question' and 'answer'"):
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node.run(file_paths=csv_path)
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@pytest.mark.integration
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def test_csv_to_document_many_files(tmp_path):
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csv_paths = []
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for i in range(5):
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node = CsvTextConverter()
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csv_path = tmp_path / f"{i}_csv_qa_with_headers.csv"
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csv_paths.append(csv_path)
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rows = [
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["question", "answer"],
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[
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f"{i}. What is Haystack ?",
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f"{i}. Haystack is an NLP Framework to use transformers in your Applications.",
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],
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]
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write_as_csv(rows, csv_path)
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output, edge = node.run(file_paths=csv_paths)
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assert edge == "output_1"
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assert "documents" in output
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assert len(output["documents"]) == 5
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for i in range(5):
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doc = output["documents"][i]
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assert isinstance(doc, Document)
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assert doc.content == f"{i}. What is Haystack ?"
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assert doc.meta["answer"] == f"{i}. Haystack is an NLP Framework to use transformers in your Applications."
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