doxstrux 0.2.1

Document structure extraction tool for markdown, with extensibility to PDF and HTML

🏗️ Doxstrux

Document structure extraction tool for markdown, with extensibility to PDF and HTML.

Extract hierarchical structure, metadata, and content from documents without semantic analysis. Built for RAG pipelines, documentation analysis, and AI preprocessing.

✨ Features

• Zero-regex parsing: Token-based extraction using markdown-it-py
• Security-first design: Three security profiles (strict/moderate/permissive)
• Document IR: Clean intermediate representation for RAG chunking
• Structure extraction: Headings, lists, tables, code blocks, links, images
• Content integrity: Parse without mutation, fail-closed security
• Extensible architecture: Ready for PDF and HTML support

📦 Installation

pip install doxstrux

🚀 Quick Start

from doxstrux.markdown_parser_core import MarkdownParserCore

# Basic usage
content = "# Hello\n\nThis is **markdown**."
parser = MarkdownParserCore(content)
result = parser.parse()

# Access structure
print(result['structure']['headings'])
print(result['metadata']['security']['statistics'])

# With security profile
parser = MarkdownParserCore(content, security_profile='strict')
result = parser.parse()

# With custom config
parser = MarkdownParserCore(
    content,
    config={
        'preset': 'gfm',
        'plugins': ['table', 'strikethrough'],
        'allows_html': False
    },
    security_profile='moderate'
)
result = parser.parse()

🏗️ Architecture

Core Principles

• Extract everything, analyze nothing: Focus on structural extraction, not semantics
• No file I/O in core: Parser accepts content strings, not paths
• Plain dict outputs: Lightweight, no heavy dependencies
• Security layered throughout: Size limits, plugin validation, content sanitization
• Modular extractors (Phase 7): 11 specialized modules with dependency injection
• Single responsibility: Each extractor handles one markdown element type

Security Profiles

Profile	Max Size	Max Lines	Recursion Depth	Use Case
strict	100KB	2K	50	Untrusted input
moderate	1MB	10K	100	Standard use (default)
permissive	10MB	50K	150	Trusted documents

Document IR

Clean intermediate representation for RAG pipelines and chunking:

from doxstrux.markdown.ir import DocumentIR, ChunkPolicy

# Parse to IR
parser = MarkdownParserCore(content)
result = parser.parse()
doc_ir = DocumentIR.from_parse_result(result)

# Apply chunking policy
policy = ChunkPolicy(
    max_chunk_tokens=512,
    overlap_tokens=50,
    respect_boundaries=['heading', 'section']
)
chunks = doc_ir.chunk(policy)

🧪 Testing

# Run all tests
pytest

# With coverage
pytest --cov=src/doxstrux

# Type checking
mypy src/doxstrux

# Linting
ruff check src/ tests/

📊 Project Status

• Version: 0.2.1 ✅ Published on PyPI
• Python: 3.12+
• Test Coverage: 69% (working toward 80% target)
• Tests: 95/95 pytest passing + 542/542 baseline tests passing
• Regex Count: 0 (zero-regex architecture)
• Core Parser: 1944 lines (reduced from 2900, -33%)
• PyPI: https://pypi.org/project/doxstrux/

Phase 7: Modular Architecture ✅ COMPLETE

Completed: Full modularization of parser into 11 specialized extractors

• ✅ 7.0.5: Rename from docpipe to doxstrux
• ✅ 7.1: Create namespace structure
• ✅ 7.2: Move existing modules to new namespace
• ✅ 7.3: Extract line & text utilities
• ✅ 7.4: Extract configuration & budgets
• ✅ 7.5: Extract simple extractors (media, footnotes, blockquotes, html)
• ✅ 7.6: Extract complex extractors (lists, codeblocks, tables, links, sections, paragraphs)

Achievements:

• Core parser reduced by 33% (2900 → 1944 lines)
• 11 specialized extractor modules created
• 100% baseline test parity maintained
• Clean dependency injection pattern throughout
• Zero behavioral changes (byte-for-byte output identical)

🗺️ Roadmap

• Phase 7: Modular architecture ✅ COMPLETE
• Phase 8: Enhanced testing & documentation
• PDF support: Extract structure from PDF documents
• HTML support: Parse HTML with same IR
• Enhanced chunking: Semantic-aware chunking strategies
• Performance: Cython optimization for hot paths

📚 Documentation

• Architecture: See CLAUDE.md for detailed architecture notes
• Phase 7 Plan: See regex_refactor_docs/DETAILED_TASK_LIST.md
• Testing: See regex_refactor_docs/REGEX_REFACTOR_POLICY_GATES.md

🤝 Contributing

This project follows a phased refactoring methodology with comprehensive test gates.

1. All changes must pass 63 pytest tests
2. All changes must maintain byte-for-byte output parity (542 baseline tests)
3. Security-first: No untrusted regex, validated links, sanitized HTML
4. Type-safe: Full mypy strict mode compliance

📜 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

Built on:

• markdown-it-py - CommonMark compliant parser
• mdit-py-plugins - Extended markdown features

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Previous name: docpipe (renamed to doxstrux in v0.2.0 for extensibility to PDF/HTML)