sluggi 0.1.0

A modern, high-performance Python library for turning text into clean, URL-safe slugs.

sluggi

✨ New Automation & Collaboration Features

• Adaptive triage workflows: Issues and PRs are now auto-labeled, parsed for agent/human status, and incomplete PRs are auto-closed for you—saving time for everyone.
• Agent-ready templates: All issue and PR templates are designed for both humans and autonomous agents, with structured metadata and feedback built in.
• Playground workflow: Safely experiment, test, or self-heal code with the new playground automation—perfect for bots and contributors alike.

See .github/workflows/README.md for more details on these next-generation automations!

sluggi — The modern, blazing-fast Python library and CLI for turning any text into clean, URL-safe slugs.

[](GitHub Releases)

The mutation and benchmark badges are generated by CI and auto-updated on main. You can also download the latest badges from the mutation and benchmark workflow run artifacts.

Inspired by slugify, reimagined for speed, Unicode, and robust parallel batch processing.

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Table of Contents

• Features
• Installation
• Usage
• API Reference
• Advanced Usage & Performance Tips
• Command-Line Interface (CLI)
• Development & Contributing
• Performance & Benchmarks
• License
• See Also

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Features

• 🚀 Fast: Optimized for speed with minimal dependencies.
• 🌍 Unicode & Emoji: Handles dozens of scripts, emoji, and edge cases out of the box.
• 🔧 Customizable: Define your own character mappings and rules.
• 🧵 Parallel Batch: True multi-core batch slugification (thread/process/serial modes).
• ⚡ Async Support: Full asyncio-compatible API for modern Python apps.
• 🖥️ CLI Tool: Powerful, colorized CLI for quick slug generation and batch jobs.
• 🔒 Safe Output: Always generates URL-safe, predictable slugs.
• 🧩 Extensible API: Easy to use and extend.
• ✅ CI & Pre-commit: Linting, formatting, and tests run automatically.

Modular Slugification Pipeline

sluggi processes text through a modular pipeline of single-responsibility functions, making the codebase more readable, maintainable, and extensible. Each step in the pipeline performs a distinct transformation, allowing for easy customization and extension.

Pipeline Steps:

1. normalize_unicode(text) Normalize Unicode characters to a canonical form (NFKC).
2. decode_html_entities_and_refs(text) Decode HTML entities and character references to their Unicode equivalents.
3. convert_emojis(text) Replace emojis with their textual representations.
4. transliterate_text(text) Transliterate non-ASCII characters to ASCII (where possible).
5. apply_custom_replacements(text, custom_map) Apply user-defined or staged character/string replacements.
6. extract_words(text, word_regex) Extract words using a customizable regex pattern.
7. filter_stopwords(words, stopwords) Remove unwanted words (e.g., stopwords) from the list.
8. join_words(words, separator) Join words using the specified separator.
9. to_lowercase(text, lowercase) Convert the result to lowercase if requested.
10. strip_separators(text, separator) Remove leading/trailing separators.
11. smart_truncate(text, max_length, separator) Optionally truncate the slug at a word boundary.

Processing Flow:

Input Text
  ↓
normalize_unicode
  ↓
decode_html_entities_and_refs
  ↓
convert_emojis
  ↓
transliterate_text
  ↓
apply_custom_replacements
  ↓
extract_words
  ↓
filter_stopwords
  ↓
join_words
  ↓
to_lowercase
  ↓
strip_separators
  ↓
smart_truncate
  ↓
Final Slug

This modular approach makes it easy to add, remove, or modify steps in the pipeline. Each function is pure and well-documented. See the API docs and source for details on customizing or extending the pipeline.

Installation

Install from PyPI:

pip install sluggi

For CLI and development:

pip install .[cli,dev]

Usage

from sluggi import slugify, batch_slugify

slug = slugify("Hello, world!")
print(slug)  # hello-world

# Batch processing (parallel by default)
slugs = batch_slugify(["Hello, world!", "Привет мир"])
print(slugs)  # ['hello-world', 'privet-mir']

# Advanced: Parallel processing
slugs = batch_slugify(["foo", "bar"], parallel=True, mode="process", workers=2)

# Stopwords (exclude common words from slugs)
slug = slugify("The quick brown fox jumps", stopwords=["the", "fox"])
print(slug)  # quick-brown-jumps

slugs = batch_slugify([
    "The quick brown fox jumps",
    "Jump over the lazy dog"
], stopwords=["the", "over", "dog"])
print(slugs)  # ['quick-brown-fox-jumps', 'jump-lazy']

# Custom regex pattern for word extraction (e.g., only extract capitalized words)
slug = slugify("The Quick Brown Fox", word_regex=r"[A-Z][a-z]+")
print(slug)  # The-Quick-Brown-Fox

# Use in batch_slugify
slugs = batch_slugify([
    "The Quick Brown Fox",
    "Jump Over The Lazy Dog"
], word_regex=r"[A-Z][a-z]+")
print(slugs)  # ['The-Quick-Brown-Fox', 'Jump-Over-The-Lazy-Dog']

Async Usage

Requires Python 3.7+

import asyncio
from sluggi import async_slugify, async_batch_slugify

async def main():
    slug = await async_slugify("Hello, world!")
    slugs = await async_batch_slugify(["Hello, world!", "Привет мир"], parallel=True)
    print(slug)   # hello-world
    print(slugs)  # ['hello-world', 'privet-mir']

asyncio.run(main())

Custom Separator

slug = slugify("Hello, world!", separator="_")
print(slug)  # hello_world

Stopwords

slug = slugify("The quick brown fox", stopwords=["the", "fox"])
print(slug)  # quick-brown

Custom Mapping

slug = slugify("ä ö ü", custom_map={"ä": "ae", "ö": "oe", "ü": "ue"})
print(slug)  # ae-oe-ue

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API Reference

slugify(text, separator="-", custom_map=None, stopwords=None, lowercase=True, word_regex=None, process_emoji=True)

• text (str): The input string to slugify.
• separator (str, default "-"): Word separator in the slug.
• custom_map (dict, optional): Custom character mappings.
• process_emoji (bool, default True): If False, disables emoji-to-name conversion for maximum performance. Useful for datasets where emoji handling is not needed.
• stopwords (Iterable[str], optional): Words to exclude from the slug (case-insensitive if lowercase=True).
• lowercase (bool, default True): Convert result to lowercase.
• word_regex (str, optional): Custom regex pattern for word extraction (default: r'\w+').
• Returns: str (slugified string)

batch_slugify(texts, separator="-", custom_map=None, stopwords=None, lowercase=True, word_regex=None, parallel=False, workers=None, mode="thread", chunk_size=1000, cache_size=2048)

• texts (Iterable[str]): List of strings to slugify.
• stopwords (Iterable[str], optional): Words to exclude from slugs.
• word_regex (str, optional): Custom regex pattern for word extraction (default: r'\w+').
• parallel (bool): Enable parallel processing.
• workers (int, optional): Number of parallel workers.
• mode (str): "thread", "process", or "serial".
• Returns: List[str] (list of slugified strings)

async_slugify(text, separator="-", custom_map=None)

• Same as slugify, but async.

async_batch_slugify(texts, ...)

• Same as batch_slugify, but async.

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Advanced Usage & Performance Tips

Skipping Emoji Handling for Maximum Speed

• By default, sluggi converts emoji to their textual names (e.g., 😎 → smiley-face) for maximum compatibility and searchability.
• For maximum performance, you can disable emoji handling entirely if you do not need emoji-to-name conversion. This avoids all emoji detection and replacement logic, providing a measurable speedup for emoji-heavy or large datasets.
• To disable emoji handling:
  • Python API: Pass process_emoji=False to slugify, batch_slugify, or any pipeline config.
  • CLI: Add the --no-process-emoji flag to your command.

Example:

slug = slugify("emoji: 😎🤖🎉", process_emoji=False)
print(slug)  # emoji

sluggi single "emoji: 😎🤖🎉" --no-process-emoji
# Output: emoji

Batch and Async Performance

• Parallel Processing:
  • For large batches, use parallel=True and tune workers and chunk_size.
  • mode="process" enables true CPU parallelism for CPU-bound workloads.
  • mode="thread" is best for I/O-bound or repeated/cached inputs.
• Caching:
  • Threaded mode enables slugification result caching for repeated or overlapping inputs.
  • Process mode disables cache (each process is isolated).
• Asyncio:
  • Use async_batch_slugify for async web servers or event-driven apps.
  • The parallel option with async batch uses a semaphore to limit concurrency, avoiding event loop starvation.
  • For best throughput, set workers to your CPU count or the number of concurrent requests you expect.

Example: Tuning Batch Processing

# Large batch, CPU-bound: use process pool
slugs = batch_slugify(my_list, parallel=True, mode="process", workers=8, chunk_size=500)

# Async batch in a web API (FastAPI, Starlette, etc.)
from sluggi import async_batch_slugify

@app.post("/bulk-slugify")
async def bulk_slugify(payload: list[str]):
    return await async_batch_slugify(payload, parallel=True, workers=8)

When to Use Serial vs Parallel vs Async

• Serial: Small batches, low latency, or single-threaded environments.
• Parallel (thread/process): Large batches, heavy CPU work, or when maximizing throughput is critical.
• Async: Integrate with modern async web frameworks, handle many concurrent requests, or avoid blocking the event loop.

See the docstrings and API reference for more details on each option.

Command-Line Interface (CLI)

Install CLI dependencies:

pip install .[cli]

Quick Start

sluggi single "Γειά σου Κόσμε"
# Output: geia-sou-kosme

sluggi single "The quick brown fox jumps" --stopwords "the,fox"
# Output: quick-brown-jumps

sluggi single "The Quick Brown Fox" --word-regex "[A-Z][a-z]+"
# Output: The-Quick-Brown-Fox

sluggi single "The Quick Brown Fox" --no-lowercase
# Output: The-Quick-Brown-Fox

sluggi batch --input names.txt --output slugs.txt
sluggi batch --input names.txt --word-regex "[A-Z][a-z]+" --no-lowercase

# Custom output formatting in batch mode:
sluggi batch --input names.txt --output-format "{line_num}: {original} -> {slug}"
# Output example:
# 1: Foo Bar -> foo-bar
# 2: Baz Qux -> baz-qux

sluggi batch --input names.txt --output-format "{slug}"
# Output: just the slug, as before

# Display results as a rich table in the console:
sluggi batch --input names.txt --display-output
# Output example (with rich):
# ┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━┓
# ┃ row_number ┃ original     ┃ slug     ┃
# ┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━┩
# │ 1          │ Foo Bar      │ foo-bar  │
# │ 2          │ Baz Qux      │ baz-qux  │
# └────────────┴──────────────┴──────────┘

Supported placeholders for --output-format:

• {slug}: The generated slug
• {original}: The original input line
• {line_num}: The 1-based line number

Note: The --display-output table uses the rich Python library. If not installed, a plain text table will be shown instead.

CLI Options

Option	Description
--separator	Separator for words in the slug (default: -).
--stopwords	Comma-separated words to exclude from slug.
--custom-map	Custom mapping as JSON, e.g. '{"ä": "ae"}'.
--word-regex	Custom regex pattern for word extraction (default: \w+).
--no-lowercase	Preserve capitalization in the slug (default: False).
--output-format	Custom output format for batch mode. Supports {slug}, {original}, {line_num}. Default: just the slug.
--display-output	Display results as a rich table in the console after batch processing.

CLI Help

sluggi --help

Error Handling Example

sluggi batch --input missing.txt
# Output:
[bold red]Input file not found: missing.txt[/bold red]

Development & Contributing

• Clone the repo and install dev dependencies:

  git clone https://github.com/blip-box/sluggi.git
  cd sluggi
  pip install .[dev,cli]
  

• Run tests and lints:

  pytest
  ruff src/sluggi tests
  black --check src/sluggi tests
  

• Pre-commit hooks:

  pre-commit install
  pre-commit run --all-files
  

• PRs and issues welcome!

Encoding Notes

• Input and output files must be UTF-8 encoded.
• On Windows, use a UTF-8 capable terminal or set the environment variable PYTHONUTF8=1 if you encounter encoding issues.

Help and Examples

• Run sluggi --help or any subcommand with --help to see detailed usage and examples directly in your terminal.

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Performance & Benchmarks

Batch slugification performance was measured using the included benchmark script:

python scripts/benchmark_batch.py

Results on 20,000 random strings:

Mode	Time (s)	Avg ms/item
Serial	0.74	0.037
Thread	0.62–0.72	0.031–0.036
Process	1.55–1.73	0.078–0.086

• Serial is fast and reliable for most workloads.
• Thread mode may be slightly faster for I/O-bound or lightweight CPU tasks (default for --parallel).
• Process mode (multiprocessing) enables true CPU parallelism, but has higher overhead and is best for very CPU-bound or expensive slugification tasks.
• Use --mode process for multiprocessing, --mode thread for threads, or --mode serial for no parallelism. Combine with --workers to tune performance.

Script location: scripts/benchmark_batch.py

Shell Completion

Enable tab-completion for your shell (bash, zsh, fish):

sluggi completion bash   # or zsh, fish
# Follow the printed instructions to enable completion in your shell

License

MIT

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[Changelog](GitHub Releases)

Note: This project is a complete rewrite, inspired by existing slugify libraries, but aims to set a new standard for speed, correctness, and extensibility in Python.

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See Also

This project was inspired by the Java library slugify by akullpp. If you need Java or Gradle support, see their documentation for advanced transliteration and custom replacements.

Example (Java):

final Slugify slg = Slugify.builder()
    .customReplacements(Map.of("Foo", "Hello", "bar", "world"))
    .customReplacement("Foo", "Hello")
    .customReplacement("bar", "world")
    .build();
final String result = slg.slugify("Foo, bar!");
// result: hello-world

For advanced transliteration in Java:

capabilities {
    requireCapability('com.github.slugify:slugify-transliterator')
}

Or add the optional dependency com.ibm.icu:icu4j to your project.