auto-wrench 0.2.0

Automated registration package for urban sensor endpoints into metadata catalogs

Wrench 🔧

A powerful framework for building automated sensor registration pipelines.

Overview

Wrench is a modular, extensible workflow framework designed to streamline the process of harvesting, enriching, and registering sensor metadata from diverse IoT sources into urban data catalogs. It provides a standardized pipeline architecture with interchangeable components to help make sensor data more discoverable and valuable.

Features

• 🔄 Automated Metadata Harvesting: Extract metadata from various IoT data sources with minimal configuration
• 📊 Standardized Data Models: Type-safe data structures using Pydantic for consistent handling of metadata
• 🔍 Advanced Classification: Group similar sensors using machine learning and taxonomy-based approaches
• ✨ Metadata Enrichment: Enhance sensor descriptions with contextual information using LLM technologies
• 🏗️ Modular Architecture: Compose workflows from interchangeable components for maximum flexibility
• 🔌 Extensible Interfaces: Easily add support for new data sources and catalog systems
• 🤖 LLM Integration: Leverage AI capabilities for automatic content generation and classification

Installation

pip install auto-wrench

To install with specific component dependencies:

pip install 'auto-wrench[teleclass]'

Core Components

Wrench consists of three main component types that can be combined in a pipeline:

1. Harvesters: Extract metadata from IoT data sources (e.g., SensorThings API)
2. Groupers: Classify and organize sensors into meaningful groups
3. Catalogers: Register the processed metadata into data catalogs (e.g., SDDI/CKAN)

Each component type follows a standardized interface, making it easy to extend with custom implementations.

Quick Start

The following example sets up a complete pipeline with a SensorThings API harvester, a TELEClass grouper for classification, and an SDDI cataloger for registration:

from wrench.cataloger import SDDICataloger
from wrench.common.pipeline import Pipeline
from wrench.grouper import TELEClassGrouper
from wrench.harvester import SensorThingsHarvester
from wrench.utils import ContentGenerator

# Initialize components with their respective configurations
harvester = SensorThingsHarvester(
    config="config/sta_config.yaml",
    content_generator=ContentGenerator(config="config/generator_config.yaml")
)

grouper = TELEClassGrouper(config="config/teleclass_config.yaml")

cataloger = SDDICataloger(config="config/sddi_config.yaml")

# Assemble and run the pipeline
pipeline = Pipeline(
    harvester=harvester,
    grouper=grouper,
    cataloger=cataloger
)

pipeline.run()

Configuration

Each component can be configured via YAML files. Here's a basic example for the SensorThings harvester:

# sta_config.yaml
base_url: "https://example.org/v1.1"
identifier: "city_sensors"
title: "City Sensor Network"
description: "Environmental sensors across the city"

pagination:
  page_delay: 0.2
  timeout: 60
  batch_size: 100

translator:
  url: "https://translate.example.org"
  source_lang: "de"

Component Overview

Harvesters

Harvesters connect to data sources and extract metadata. Wrench includes:

• SensorThingsHarvester: Connects to OGC SensorThings API endpoints
• Extensible base class for creating custom harvesters

Groupers

Groupers organize sensors into logical groups:

• TELEClassGrouper: Taxonomy-enhanced classification using LLMs and corpus-based methods
• Can be extended with custom grouping algorithms

Catalogers

Catalogers register metadata into data catalogs:

• SDDICataloger: Registers metadata into SDDI/CKAN-based catalogs

• Extensible interface for supporting other catalog systems

Advanced Features

Translation Support

Wrench includes built-in support for translating metadata using services like LibreTranslate:

# Translation is configured in the harvester configuration
translator:
  url: "https://translate.example.org"
  source_lang: "auto"  # Automatically detect source language

LLM-Enhanced Content Generation

Generate rich descriptions for sensor groups using LLM services:

content_generator = ContentGenerator(config="config/generator_config.yaml")
harvester = SensorThingsHarvester(
    config="config/sta_config.yaml",
    content_generator=content_generator
)

Development

Setting up the Development Environment

# Clone the repository
git clone https://github.com/yourusername/wrench.git
cd wrench

# Run the make target
make setup

# Install component dependencies
uv pip install -e ".[teleclass,sensorthings]"

Code Style

This project follows the Ruff code style. Format your code using:

ruff format .
ruff check .

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please ensure your code follows our coding standards and includes appropriate tests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support and Documentation

For support, please:

• Open an issue in the GitHub repository
• Check the documentation
• Contact the development team at [e-mail]