contextifier 0.2.5

Convert raw documents into AI-understandable context with intelligent text extraction, table detection, and semantic chunking

Contextifier v2

Contextifier is a Python document processing library that converts documents of various formats into structured, AI-ready text. It applies a uniform 5-stage pipeline to every document format, ensuring consistent and predictable output.

Key Features

• Broad Format Support: PDF, DOCX, DOC, PPTX, PPT, XLSX, XLS, HWP, HWPX, RTF, CSV, TSV, TXT, MD, HTML, images, code files, and 80+ extensions
• Intelligent Text Extraction: Preserves document structure (headings, tables, image positions) with automatic metadata extraction
• Table Processing: Converts tables to HTML/Markdown/Text with rowspan/colspan support for merged cells
• OCR Integration: 5 Vision LLM engines — OpenAI, Anthropic, Google Gemini, AWS Bedrock, vLLM
• Smart Chunking: 4 strategies with automatic selection — table-aware, page-boundary, protected-region, and recursive splitting
• Immutable Config System: Frozen dataclass-based ProcessingConfig controls all behavior

Installation

pip install contextifier

or

uv add contextifier

Quick Start

1. Basic Text Extraction

from contextifier_new import DocumentProcessor

processor = DocumentProcessor()
text = processor.extract_text("document.pdf")
print(text)

2. Extract + Chunk in One Step

from contextifier_new import DocumentProcessor

processor = DocumentProcessor()
result = processor.extract_chunks("document.pdf")

for i, chunk in enumerate(result.chunks, 1):
    print(f"Chunk {i}: {chunk[:100]}...")

# Save as Markdown files
result.save_to_md("output/chunks")

3. Custom Configuration

from contextifier_new import DocumentProcessor
from contextifier_new.config import ProcessingConfig, ChunkingConfig, TagConfig

config = ProcessingConfig(
    tags=TagConfig(page_prefix="<page>", page_suffix="</page>"),
    chunking=ChunkingConfig(chunk_size=2000, chunk_overlap=300),
)

processor = DocumentProcessor(config=config)
text = processor.extract_text("report.xlsx")

4. OCR Integration

from contextifier_new import DocumentProcessor
from contextifier_new.ocr.engines import OpenAIOCREngine

ocr = OpenAIOCREngine.from_api_key("sk-...", model="gpt-4o")
processor = DocumentProcessor(ocr_engine=ocr)

text = processor.extract_text("scanned.pdf", ocr_processing=True)

Supported Formats

Category	Extensions	Notes
Documents	.pdf, .docx, .doc, .hwp, .hwpx, .rtf	HWP 5.0+, HWPX supported
Presentations	.pptx, .ppt	Slides, notes, and charts extracted
Spreadsheets	.xlsx, .xls, .csv, .tsv	Multi-sheet, formulas, charts
Text	.txt, .md, .log, .rst	Auto encoding detection
Web	.html, .htm, .xhtml	Table/structure preservation
Code	.py, .js, .ts, .java, .cpp, .go, .rs, etc. (20+)	Language-aware highlighting
Config	.json, .yaml, .toml, .ini, .xml, .env	Structure preservation
Images	.jpg, .png, .gif, .bmp, .webp, .tiff	Requires OCR engine

Architecture

contextifier_new/
├── document_processor.py     # Facade: single public entry point
├── config.py                 # Immutable config system (ProcessingConfig)
├── types.py                  # Shared types / Enums / TypedDicts
├── errors.py                 # Unified exception hierarchy
│
├── handlers/                 # 14 format-specific handlers
│   ├── base.py               #   BaseHandler — enforces 5-stage pipeline
│   ├── registry.py           #   HandlerRegistry — extension → handler mapping
│   ├── pdf/                  #   PDF (default)
│   ├── pdf_plus/             #   PDF (advanced: table detection, complex layouts)
│   ├── docx/ doc/ pptx/ ppt/ #   Office documents
│   ├── xlsx/ xls/ csv/       #   Spreadsheets / data
│   ├── hwp/ hwpx/            #   Korean word processor
│   ├── rtf/ text/            #   RTF / text / code / config
│   └── image/                #   Image (OCR integration)
│
├── pipeline/                 # 5-Stage pipeline ABCs
│   ├── converter.py          #   Stage 1: Binary → Format Object
│   ├── preprocessor.py       #   Stage 2: Preprocessing
│   ├── metadata_extractor.py #   Stage 3: Metadata extraction
│   ├── content_extractor.py  #   Stage 4: Text / table / image / chart extraction
│   └── postprocessor.py      #   Stage 5: Final assembly & cleanup
│
├── services/                 # Shared services (DI)
│   ├── tag_service.py        #   Page / slide / sheet tag generation
│   ├── image_service.py      #   Image saving / tagging / deduplication
│   ├── chart_service.py      #   Chart data formatting
│   ├── table_service.py      #   Table HTML / MD rendering
│   ├── metadata_service.py   #   Metadata formatting
│   └── storage/              #   Storage backends (Local, MinIO, S3, ...)
│
├── chunking/                 # Chunking subsystem
│   ├── chunker.py            #   TextChunker — auto strategy selection
│   ├── constants.py          #   Protected region patterns
│   └── strategies/           #   4 chunking strategies
│       ├── plain_strategy.py     # Recursive splitting (default fallback)
│       ├── table_strategy.py     # Sheet / table-based splitting
│       ├── page_strategy.py      # Page-boundary splitting
│       └── protected_strategy.py # Protected region preservation
│
└── ocr/                      # OCR subsystem (optional)
    ├── base.py               #   BaseOCREngine ABC
    ├── processor.py          #   OCRProcessor — tag detection + engine call
    └── engines/              #   5 engine implementations
        ├── openai_engine.py
        ├── anthropic_engine.py
        ├── gemini_engine.py
        ├── bedrock_engine.py
        └── vllm_engine.py

Requirements

• Python 3.12+
• Required dependencies are included in pyproject.toml
• Optional: LibreOffice (DOC/PPT/RTF conversion), Poppler (PDF image extraction)

Documentation

Document	Contents
QUICKSTART.md	Detailed usage guide & full API reference
Process Logic.md	Handler processing flow diagrams
ARCHITECTURE.md	Internal architecture specification
CHANGELOG.md	Version history
CONTRIBUTING.md	Contribution guidelines

License

Apache License 2.0 — see LICENSE

Contributing

Contributions are welcome! See CONTRIBUTING.md.