Welcome to Tikara’s documentation!#

Tikara#

Tikara Logo

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🚀 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!

🛠️ Installation#

pip install tikara

System Dependencies#

Required Dependencies#

  • Python 3.12+

  • Java Development Kit 11+ (OpenJDK recommended)

Optional Dependencies#
Geospatial Enhancements#

  • GDAL for geospatial file support (Reference ⇗)

    # Ubuntu
apt-get install gdal-bin

📖 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:

    curl -LsSf https://astral.sh/uv/install.sh | sh

  3. Install python dependencies and create the Virtual Environment:

    make install

Common Tasks#

Run make (or make help) to see all available targets. The most common ones:

# Setup
make install         # Install all dependencies (including dev)
make stubs           # Regenerate Java type stubs from the Tika JAR

# Lint & Format
make lint            # Run ruff linter (with auto-fix)
make format          # Run ruff formatter
make ruff            # Run linter and formatter together

# Test
make test            # Run tests with verbose output
make test-fast       # Run tests, skip slow benchmark/isolated markers
make test-coverage   # Run tests with coverage report (XML + terminal)

# Docs
make docs            # Build Sphinx HTML docs
make docs-open       # Build docs and open in browser

# Security
make safety          # Run safety dependency vulnerability scan

# Build & Release
make build           # Build sdist and wheel
make clean           # Remove build artifacts, caches, and generated reports

# CI / Pre-push
make ci              # Run full CI suite (lint → test → safety → docs)
make prepush         # Alias for ci — run before pushing

🤔 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 full CI suite (lint, test, coverage, safety, docs)

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.

API Reference#

Contents:

External Resources#