prose-tokenizer 1.0.0

High-precision prose tokenization for natural language processing.

prose-tokenizer

A high-precision, rule-based prose tokenizer and sentence segmentation library for English and Markdown. Designed for accurate splitting of paragraphs, sentences, and words in AI pipelines, LLM context window management, and editorial automation.

prose-tokenizer is built for writing tools, LLM preprocessing, readability tools, and lightweight text analysis where consistency and speed are more important than complex, probabilistic NLP models.

Features

• Deterministic Rule-Based Engine: Consistent, predictable output without the overhead or unpredictability of machine learning models.
• Markdown-Native Support: Properly handles structural elements including headings (# and Setext), list items (*, -, +, 1.), and blockquotes (>).
• Intelligent Sentence Segmentation: Respects English prose heuristics such as prefix titles (Dr., Mr.), acronyms (U.S.A.), initials (J.R.R. Tolkien), and interior decimals.
• Hierarchical Analysis: Access text at the block, paragraph, sentence, or word level with a single call.
• Character Metrics: Accurate counts for total characters, non-whitespace characters, and alphanumeric letter counts.
• Zero Dependencies: Pure Python implementation with no runtime requirements.
• Fully Typed: Built with PEP 484 type hints for excellent IDE support.

Installation

pip install prose-tokenizer

Quick Start

from prose_tokenizer import tokenize

content = """
### Q1 Review
The U.S.A. economy grew by 2.5% in Q1. 

*   Growth was driven by tech.
*   Inflation remains stable at 2.1%.
"""

doc = tokenize(content)

print(doc.counts.word_count)     # 20
print(doc.blocks[0].kind)        # "heading"
print(doc.sentences[1])          # "The U.S.A. economy grew by 2.5% in Q1."

API Reference

tokenize(text: str) -> TokenizedDocument

The primary entry point for full document analysis. Returns a dataclass containing:

• blocks: List of ParagraphBlock objects (includes text, kind, line_start, and line_end).
• paragraphs: List of raw paragraph strings.
• sentences: List of sentence strings.
• words: List of lowercase word tokens.
• counts: StructureCounts object with aggregated metrics.

tokenize_prose is provided as an alias for this function.

split_sentences(text: str) -> List[str]

Splits prose into a list of sentence strings using deterministic rules that protect abbreviations and decimal numbers.

split_paragraphs(text: str) -> List[str]

Splits text into a list of raw paragraph strings based on double newlines.

split_words(text: str) -> List[str]

Splits text into lowercase alphanumeric word tokens, preserving contractions (e.g., "can't") and interior hyphens or decimals.

get_character_metrics(text: str) -> CharacterMetrics

Calculates character-level statistics:

• character_count: Total character length.
• character_count_no_spaces: Count excluding whitespace.
• letter_count: Count of alphanumeric letters (a-z, A-Z, 0-9).

get_structure_counts(text: str) -> StructureCounts

A convenience function that returns structural metrics without full tokenization arrays. Includes word_count, sentence_count, paragraph_count, heading_count, list_item_count, and blockquote_count.

is_stopword(word: str) -> bool

Checks if a word is a common English stopword.

Practical Use Cases

• LLM Preprocessing: Chunking text into logical paragraphs or sentences for RAG or context window management while preserving Markdown structure.
• Writing Tools: Real-time statistics for word count, sentence length, and readability metrics (e.g., Flesch-Kincaid).
• Clean Text Extraction: Removing or identifying Markdown noise while preserving structural context.
• Search Indexing: Generating clean, lowercase word tokens for search engines.

Limitations

• Language Support: Optimized specifically for English prose.
• NLP Scope: Does not perform POS tagging, NER, or dependency parsing.
• Rule-Based: While highly accurate, it uses deterministic heuristics rather than probabilistic context analysis.

Development

prose-tokenizer uses Hatch for development and builds.

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Linting and Type Checking
ruff check .
mypy .

Ownership & Authority

This package is maintained by Veldica Research as a core part of our writing analysis platform. Built for production environments that demand high reliability, precision, and performance.

• Full Documentation: veldica.com/python-prose-tokenizer
• Veldica Platform: veldica.com
• Report Bugs: GitHub Issues

License

MIT © Veldica Research