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Changelog

All notable changes to the ODPS Python library will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased] - v4.1 Support

🎉 ODPS v4.1 Support

Updated to add support for ODPS v4.1 specification with ProductStrategy, AI agent integration, and enhanced referencing capabilities.

✨ New Features (v4.1)

ProductStrategy Component

  • Added ProductStrategy dataclass for connecting data products to business objectives
  • Added KPI dataclass for defining Key Performance Indicators
  • Support for business objectives, contributesToKPI, productKPIs, and relatedKPIs
  • Strategic alignment tracking with corporate initiatives
  • Added KPIDirection enum: increase, decrease, at_least, at_most, equals
  • Added KPIUnit enum: percentage, minutes, seconds, count, currency, and more
  • Comprehensive validation for ProductStrategy and KPI fields
  • Added ProductStrategyValidator to validation framework

AI Agent Integration

  • Added AI output port type for AI agent data access
  • Support for Model Context Protocol (MCP) specification
  • AI agent-native delivery mechanisms
  • Autonomous machine consumption patterns

Enhanced $ref Support

  • Added dollar_ref field to DataContract, SLA, DataQuality, DataAccess, and PaymentGateway models
  • JSON Reference syntax support for internal references (#/product/...)
  • JSON Reference syntax support for external URL-based references
  • DRY principle implementation for component reusability

Schema Updates

  • Updated schema URL to https://opendataproducts.org/v4.1/schema/odps.json
  • Updated version to 4.1
  • Full backward compatibility with v4.0 documents

🔧 Technical Improvements

  • Added field mappings for ProductStrategy serialization (snake_case ↔ camelCase)
  • Extended OpenDataProduct.__slots__ with product_strategy field
  • Updated _generate_hash() to include product_strategy for cache invalidation
  • Enhanced to_dict() with ProductStrategy and nested KPI serialization
  • Enhanced from_dict() with ProductStrategy and KPI parsing
  • Updated all component docstrings to reference v4.1

📚 Documentation

  • Updated README.md with v4.1 feature overview
  • Added comprehensive v4.1 example (examples/odps_v41_example.py)
  • Updated Quick Start guide with ProductStrategy usage
  • Updated Optional Components list with v4.1 additions
  • Added links to v4.0 → v4.1 migration guide

🔄 Migration Notes

  • Fully backward compatible - v4.0 documents work without modification
  • ProductStrategy is optional - existing code continues to function
  • New KPI enums available but optional
  • AI output port type is optional enhancement
  • $ref support is optional feature

[0.1.0] - 2024-08-19

🎉 Initial Release

A comprehensive Python library for creating, validating, and manipulating Open Data Product Specification (ODPS) v4.0 documents with full international standards compliance.

✨ Core Features

  • Complete ODPS v4.0 Support: Full implementation of the Open Data Product Specification v4.0
  • International Standards Compliance: Built-in validation for ISO, RFC, and ITU-T standards
  • High Performance: Optimized with caching, __slots__, and efficient validation patterns
  • Type Safety: Comprehensive type hints, protocols, and runtime type checking
  • Modular Architecture: Pluggable validators and extensible component system

🌍 International Standards Validation

  • ISO 639-1: Language code validation for multilingual fields
  • ISO 3166-1 alpha-2: Country code validation
  • ISO 4217: Currency code validation for pricing plans
  • ISO 8601: Date/time format validation
  • E.164: International phone number format validation
  • RFC 5322: Email address validation
  • RFC 3986: URI/URL validation for all link fields

🏗️ Architecture & Design

  • Protocol-Based Architecture: Type-safe interfaces using Python protocols
  • Validation Framework: Modular, pluggable validation system
  • Exception Hierarchy: Comprehensive error handling with 20+ specialized exceptions
  • Performance Optimizations: Caching system with hash-based invalidation
  • Memory Efficiency: __slots__ implementation across all models

📦 Components Supported

  • ProductDetails: Core product information with all ODPS v4.0 attributes
  • DataAccess: Data access methods with required default method enforcement
  • DataHolder: Complete contact and organizational information
  • License: Comprehensive licensing terms and conditions
  • PricingPlans: Flexible pricing with multi-currency support
  • DataContract: Contract specifications with ODCS/DCS support
  • SLA & DataQuality: Service level and quality profiles
  • PaymentGateway: Payment processing integration
  • SpecificationExtensions: Custom x- prefixed fields support

🔧 Technical Features

  • Smart Properties: is_valid, validation_errors, compliance_level
  • Serialization: JSON/YAML export with proper camelCase conversion
  • File I/O: Load/save from files with automatic format detection
  • Multilingual Support: Full support for multilingual dictionaries
  • Caching System: Up to 45x performance improvement on repeated operations

🧪 Quality & Testing

  • Comprehensive test suite with >90% coverage
  • Protocol compliance testing
  • Performance benchmarking
  • International standards validation testing
  • Example scripts and documentation

📖 Documentation & Examples

  • Complete API documentation with docstrings
  • Basic usage examples in examples/basic_usage.py
  • Advanced features demo in examples/advanced_features.py
  • Comprehensive example in examples/comprehensive_example.py
  • Demo ODPS documents in JSON and YAML formats

📋 Dependencies

  • Core: PyYAML>=6.0.2
  • Standards Validation: pycountry>=24.6.1, phonenumbers>=9.0.11
  • Development: Full development toolkit with testing, linting, and type checking

🚀 Getting Started

from odps import OpenDataProduct, ProductDetails

# Create a new ODPS document
product = ProductDetails(
    name="My Data Product",
    product_id="my-product-001",
    visibility="public",
    status="production",
    type="dataset"
)

odp = OpenDataProduct(product)
odp.validate()  # Full standards compliance validation
print(odp.to_json())  # Export to JSON

For complete documentation and examples, see the README and examples/ directory.