deckard 0.1.0

Extract structured data from unstructured text — no AI, just regular expressions. 🔍

Deckard 🕵️‍♂️

Extract structured data from unstructured text — no AI, just regular expressions. 🔍

Deckard is a library of regular-expression patterns for extracting structured data (addresses, phone numbers, email addresses, etc.) and a small set of helper utilities that make using those patterns easier.

Status: very early-stage project. Right now the repository contains mostly patterns for Poland. I am looking for contributors from around the world 🌍 — address formats, phone-number formats and other data representations differ by country, so the goal is to gather country-specific patterns for many regions.

Key features ✨

• 🗂️ A collection of ready-to-use regex patterns organized by country (for example deckard/patterns/pl.py).
• 📦 Universal patterns (e.g. email) live in deckard/patterns/standard.py.
• 🛠️ A small helper function deckard.search that combines multiple patterns and returns named-group matches (deckard/main.py).

Installation ⚙️

From PyPI:

pip install deckard

Editable / local development install:

pip install -e .

For contributors — install dependencies with Poetry 🧑‍💻

This project uses Poetry to manage dependencies and development dependencies.

1. Install Poetry (see https://python-poetry.org for instructions).
2. From the project root run:

poetry install

This will create a virtual environment and install runtime and development dependencies (including pytest).

To run tests using Poetry:

poetry run pytest

Or start a shell in the created virtualenv and run tests directly:

poetry shell
pytest

Quick usage 🧭

Example using the current public API:

from deckard import search
from deckard.patterns import standard, pl

text = (
    "Hello, my email is [e-mail] and my phone number is "
    "[phone] and my address is ul. Tesotowa 12/6A, 66-700 Bielsko-Biała."
)

result = search([standard.EMAIL, pl.MOBILE_PHONE, pl.ADDRESS], text)

# result.groupdict() will return a dict of named groups, for example:
# {
#   'email': '[e-mail]',
#   'mobile_phone': '792 321 321',
#   'street': 'ul. Tesotowa',
#   'building': '12',
#   'apartment': '6A',
#   'zip_code': '66-700',
#   'city': 'Bielsko-Biała'
# }

The search helper composes the provided patterns into a single regex (using lookaheads) and returns the first match as a regex.Match object (or None if nothing matched).

Repository layout

• deckard/ — library code
  • deckard/main.py — helper search function
  • deckard/patterns/standard.py — universal patterns (e.g. EMAIL)
  • deckard/patterns/pl.py — Poland-specific patterns (address, postal code, phone, etc.)
• tests/ — unit tests

Examples of existing tests:

• tests/test_standard_patterns.py — test for standard.EMAIL
• tests/test_search_with_multiple_patterns.py — integration tests combining standard.EMAIL with patterns from pl.py
• tests/pl/test_search_address_pl.py — tests for Polish address patterns

Every new pattern must come with tests. Pull requests without tests will not be accepted.

Contributing — how to add new patterns

1. Create a new file under deckard/patterns/ named by the country code, e.g. us.py, de.py, fr.py.
2. Define constants (UPPERCASE) for each pattern, for example MOBILE_PHONE, ADDRESS, ZIP_CODE.
3. Add tests under tests/. Use the existing Polish tests (e.g. tests/test_search_with_multiple_patterns.py) as a template. Provide normal and edge-case examples.
4. In the PR description explain local rules (phone number format, postal code format, common street abbreviations, etc.).
5. PRs without tests will not be accepted.

Tips 💡:

• 🧾 Use clear, consistent named groups in regexes (?P<name>) so groupdict() returns a predictable structure.
• 📝 Document complex patterns with comments and example inputs if necessary.

Discussion and roadmap 🚧

The project is not yet final — everything is open for discussion. Areas for contributors and discussion include:

• 📋 Defining a minimal set of patterns every country should provide (email, phone, address, postal code, national ID where applicable).
• 🔠 Standardizing group names (street, building, apartment, zip_code, city, country, mobile_phone, etc.).
• ⚖️ Tools for validation and normalization of extracted values.
• 🤖 Automating tests with sample documents in various languages.

If you want to help, open an issue or a PR — a short description of the local data format and one or two patterns with tests is a great place to start.

License 📄

This project is licensed under the MIT License. See the LICENSE file for the full text.

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Thanks for your interest — please join the effort. Together we can build an international library of patterns to extract structured data from arbitrary text using robust regular expressions. 🚀