Redlines produces a text showing the differences between two strings/text. The changes are represented with strike-throughs and underlines, which looks similar to Microsoft Word's track changes. This method of showing changes is more familiar to lawyers and is more compact for long series of characters.

Redlines uses SequenceMatcher to find differences between words used. The output can be in HTML, Markdown, or rich format.

Given an original string:

The quick brown fox jumps over the lazy dog.

And the string to be tested with:

The quick brown fox walks past the lazy dog.

The library gives a result of:

The quick brown fox <del>jumps over </del><ins>walks past </ins>the lazy dog.

Which is rendered like this:

The quick brown fox jumps over walks past the lazy dog.

The library can also output the results in Markdown, HTML or rich format, and for a variety of environments like Streamlit, Jupyter Notebooks, Google Colab and the terminal.

pip install redlines

For advanced sentence boundary detection (requires Python 3.11+):

pip install redlines[nupunkt]

The NupunktProcessor provides intelligent sentence tokenization that handles:

• Abbreviations (Dr., Mr., etc.)
• Decimals and numbers (3.14, $5.99)
• URLs and email addresses
• Legal citations and complex punctuation

See the Usage section below for more details.

The library contains one class: Redlines, which is used to compare text.

from redlines import Redlines

test = Redlines(
    "The quick brown fox jumps over the lazy dog.",
  "The quick brown fox walks past the lazy dog.", markdown_style="none",
)
assert (
        test.output_markdown
        == "The quick brown fox <del>jumps over </del><ins>walks past </ins>the lazy dog."
)

Alternatively, you can create Redline with the text to be tested, and compare several times to see the results.

from redlines import Redlines

test = Redlines("The quick brown fox jumps over the lazy dog.", markdown_style="none")
assert (
        test.compare("The quick brown fox walks past the lazy dog.")
        == "The quick brown fox <del>jumps over </del><ins>walks past </ins>the lazy dog."
)

assert (
        test.compare("The quick brown fox jumps over the dog.")
        == "The quick brown fox jumps over the <del>lazy </del>dog."
)

Redlines supports custom processors for different tokenization strategies. By default, it uses WholeDocumentProcessor which tokenizes at the paragraph level.

For sentence-level tokenization with intelligent boundary detection (requires pip install redlines[nupunkt]):

from redlines import Redlines
from redlines.processor import NupunktProcessor

# Use NupunktProcessor for better handling of abbreviations and complex punctuation
processor = NupunktProcessor()
test = Redlines(
    "Dr. Smith said hello. Mr. Jones replied.",
    "Dr. Smith said hi. Mr. Jones replied.",
    processor=processor
)

When to use NupunktProcessor:

• Legal or technical documents with many abbreviations
• Text with URLs, emails, or complex citations
• When you need sentence-level granularity
• Documents with decimal numbers that shouldn't be treated as sentence boundaries

When to use WholeDocumentProcessor (default):

• Simple documents without complex sentence structures
• When speed is critical (5-6x faster than NupunktProcessor)
• When paragraph-level granularity is sufficient

See the demo comparison for detailed performance and accuracy benchmarks.

Redlines also features a simple command line tool redlines to visualise the differences in text in the terminal.

 Usage: redlines text [OPTIONS] SOURCE TEST

 Compares the strings SOURCE and TEST and produce a redline in the terminal.

You may also want to check out the demo project redlines-textual.

Read the available Documentation.

• View and mark changes in legislation: PLUS Explorer
• Visualise changes after ChatGPT transforms a text: ChatGPT Prompt Engineering for Developers Lesson 6

MIT License