darn-it

piccy (darn - Welsh, 'därn', meaning 'piece' or more favourably, 'chunk')

Darn is a python tool for producing optimal 'chunks' from markdown-formatted string data. The tool makes use of a novel 'punishment' based approach to determining chunk boundaries, which in practice can be seen to be an improvement over traditional "semantic" chunking methods. To avoid adding unexpected behaviours (hidden text cleaning etc...), darn assumes ASCII compliance. pre-cleaning to remove non-ASCII compliant characters should be performed by users of the package ahead of time to avoid errors in processing.

for more on the method that underpins darn, see the accompanying blog post. for detailed examples, see the documentation.

Setup

To use darn, you must first install it, ideally into a python virtual environment:

pip install uv

uv venv --python 3.13
source .venv/bin/activate # or .venv\Scripts\activate on windows

uv pip install darn_it

from here, you can import it into your python session for use:

from darn_it import (
    Chunker,
    PyNodeType,
)

chunker = Chunker(
    [
        Rule(
            on_punishment="const",
            on_scale=10,
            off_punishment="const",
            off_scale=0,
            nodetype=PyNodeType.Sentence
        ),  # ... add custom rules here. if none are provided, darn will fall back on its curated list of defaults
    ]
)

with open("file.md", "r") as f:
    text = f.read()

chunks = chunker.get_chunks(
    text=text, 
    chunk_size=500,
    # granularity="tokens", # either "tokens" or "characters"
    # model="gpt-4o", # only consumed if `granularity` is set to `tokens`
    # overlap=50, # respects `granularity` variable
    # return_vectors=true, # turn on to include the full punishment vectors per rule in the output object
)

Rules can be applied to any structure from the mdast specification, along with the Sentence and Word. A full list of currently supported punishment functions for rules is given below:

punishment meaning
const a static punishment value
linear a punishment value that increments by 1 per character, starting at the provided input
reverse_linear a punishment that decrements by 1 per character, starting at the provided input and stopping at 0
triangular a punishment which peaks at the provided value in the middle of the structure, starting and ending at 0
inverse_triangular a punishment which peaks at the start and end of the structure at the provided value, and hits 0 in the middle

When settling on rules, you may find the punishments and punishment_breakdown attributes of the output object from get_chunks() to be of use. To access them, turn the return_vectors argument to True. The punishment_breakdown shows the punishments attributable to each rule in order whilst the punishments attribute gives the summed punishment across all rules. these are really intended for analytical use, not to be left on in production.

Key dates

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  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for JohnCLStokes from gravatar.com JohnCLStokes

Credits

Author: John C Ll Stokes

Requires

Python >=3.9

Classifiers

Report project as malware

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This release

1.3.2 This release

95 files

1.3.1

97 files

1.2.0

97 files

1.0.0

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0.2.4

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0.2.3

97 files

0.2.2

97 files

0.2.1

97 files

0.1.2

100 files

0.1.1

111 files

0.1.0

111 files

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