tikara 0.1.6

The metadata and text content extractor for almost every file type.

Tikara

🚀 Overview

Tikara is a modern, type-hinted Python wrapper for Apache Tika, supporting over 1600 file formats for content extraction, metadata analysis, and language detection. It provides direct JNI integration through JPype for optimal performance.

from tikara import Tika

tika = Tika()
content, metadata = tika.parse("document.pdf")

⚡️ Key Features

• Modern Python 3.12+ with complete type hints
• Direct JVM integration via JPype (no HTTP server required)
• Streaming support for large files
• Recursive document unpacking
• Language detection
• MIME type detection
• Custom parser and detector support
• Comprehensive metadata extraction
• Ships with embedded Tika JAR: works in air-gapped networks. No need to manage libraries.
• Opinionated Pydantic wrapper over Tika's metadata model, with access to the raw metadata.

📦 Supported Formats

🌈 1682 supported media types and counting!

• See the full list →
• Tika parsers list ⇗

🛠️ Installation

pip install tikara

System Dependencies

Required Dependencies

• Python 3.12+
• Java Development Kit 11+ (OpenJDK recommended)

Optional Dependencies

Image and PDF OCR Enhancements (recommended)

• Tesseract OCR (strongly recommended if you process images) (Reference ⇗)

  # Ubuntu
  apt-get install tesseract-ocr
  

  Additional language packs for Tesseract (optional):

  # Ubuntu
  apt-get install tesseract-ocr-deu tesseract-ocr-fra tesseract-ocr-ita tesseract-ocr-spa
  

• ImageMagick for advanced image processing (Reference ⇗)

  # Ubuntu
  apt-get install imagemagick
  

Multimedia Enhancements (recommended)

• FFMPEG for enhanced multimedia file support (Reference ⇗)

  # Ubuntu
  apt-get install ffmpeg
  

Enhanced PDF Support (recommended)

• PDFBox ⇗ for enhanced PDF support (Reference ⇗)

  # Ubuntu
  apt-get install pdfbox
  

Enhanced PDF support with PDFBox Reference ⇗

Metadata Enhancements (recommended)

• EXIFTool for metadata extraction from images Reference ⇗

  # Ubuntu
  apt-get install libimage-exiftool-perl
  

Geospatial Enhancements

• GDAL for geospatial file support (Reference ⇗)

  # Ubuntu
  apt-get install gdal-bin
  

Additional Font Support (recommended)

• MSCore Fonts for enhanced Office file handling (Reference ⇗)

  # Ubuntu
  apt-get install xfonts-utils fonts-freefont-ttf fonts-liberation ttf-mscorefonts-installer
  

For more OS dependency information including MSCore fonts setup and additional configuration, see the official Apache Tika Dockerfile.

📖 Usage

Example Jupyter Notebooks 📔

Basic Content Extraction

from tikara import Tika
from pathlib import Path

tika = Tika()

# Basic string output
content, metadata = tika.parse("document.pdf")

# Stream large files
stream, metadata = tika.parse(
    "large.pdf",
    output_stream=True,
    output_format="txt"
)

# Save to file
output_path, metadata = tika.parse(
    "input.docx",
    output_file=Path("output.txt"),
    output_format="txt"
)

Language Detection

from tikara import Tika

tika = Tika()
result = tika.detect_language("El rápido zorro marrón salta sobre el perro perezoso")
print(f"Language: {result.language}, Confidence: {result.confidence}")

MIME Type Detection

from tikara import Tika

tika = Tika()
mime_type = tika.detect_mime_type("unknown_file")
print(f"Detected type: {mime_type}")

Recursive Document Unpacking

from tikara import Tika
from pathlib import Path

tika = Tika()
results = tika.unpack(
    "container.docx",
    output_dir=Path("extracted"),
    max_depth=3
)

for item in results:
    print(f"Extracted {item.metadata['Content-Type']} to {item.file_path}")

🔧 Development

Environment Setup

1. Ensure that you have the system dependencies installed

2. Install uv:

   pip install uv
   

3. Install python dependencies and create the Virtual Environment: uv sync

Common Tasks

make ruff        # Format and lint code
make test        # Run test suite
make docs        # Generate documentation
make stubs       # Generate Java stubs
make prepush     # Run all checks (ruff, test, coverage, safety)

🤔 When to Use Tikara

Ideal Use Cases

• Python applications needing document processing
• Microservices and containerized environments
• Data processing pipelines (Ray, Dask, Prefect)
• Applications requiring direct Tika integration without HTTP overhead

Advanced Usage

For detailed documentation on:

• Custom parser implementation
• Custom detector creation
• MIME type handling

See the Example Jupyter Notebooks 📔

🎯 Inspiration

Tikara builds on the shoulders of giants:

• Apache Tika - The powerful content detection and extraction toolkit
• tika-python - The original Python Tika wrapper using HTTP that inspired this project
• JPype - The bridge between Python and Java

Considerations

• Process isolation: Tika crashes will affect the host application
• Memory management: Large documents require careful handling
• JVM startup: Initial overhead for first operation
• Custom implementations: Parser/detector development requires Java interface knowledge

📊 Performance Considerations

Memory Management

• Use streaming for large files
• Monitor JVM heap usage
• Consider process isolation for critical applications

Optimization Tips

• Reuse Tika instances
• Use appropriate output formats
• Implement custom parsers for specific needs
• Configure JVM parameters for your use case

🔐 Security Considerations

• Input validation
• Resource limits
• Secure file handling
• Access control for extracted content
• Careful handling of custom parsers

🤝 Contributing

Contributions welcome! The project uses Make for development tasks:

make prepush     # Run all checks (format, lint, test, coverage, safety)

For developing custom parsers/detectors, Java stubs can be generated:

make stubs       # Generate Java stubs for Apache Tika interfaces

Note: Generated stubs are git-ignored but provide IDE support and type hints when implementing custom parsers/detectors.

Common Problems

• Verify Java installation and JAVA_HOME environment variable
• Ensure Tesseract and required language packs are installed
• Check file permissions and paths
• Monitor memory usage when processing large files
• Use streaming output for large documents

📚 Reference

See API Documentation for complete details.

📄 License

Apache License 2.0 - See LICENSE for details.