TODO.md

USDA FDC Python Library - Development Roadmap

This document outlines the development plan for the usda_fdc Python library.

Phase 1: Core API Implementation

• API Client

  • Create base client class with authentication handling
  • Implement rate limiting and error handling
  • Add support for all FDC API endpoints
  • Implement pagination helpers

• Data Models

  • Create base models for all FDC data types
  • Implement serialization/deserialization
  • Add validation for API responses
  • Create comprehensive nutrient data models

• Search Functionality

  • Implement basic search with filtering
  • Add support for advanced search operators
  • Create helper methods for common search patterns

Phase 2: Django Integration

• Django Models

  • Create Django models that mirror FDC data structures
  • Implement migration scripts
  • Add indexes for efficient querying

• Caching Layer

  • Implement caching mechanism for API responses
  • Add cache invalidation strategies
  • Create background tasks for cache warming/refreshing

• Admin Interface

  • Create Django admin views for food data
  • Add custom filters and search functionality
  • Implement bulk operations

Phase 3: Advanced Features

• Unit Conversion

  • Implement comprehensive unit conversion system
  • Support for common food measurement conversions
  • Add portion size calculations

• Nutrient Analysis

  • Create tools for analyzing nutrient content
  • Implement RDA/DRI comparison functionality
  • Add visualization helpers

• Batch Processing

  • Implement efficient batch API operations
  • Add background processing for large datasets
  • Create export functionality (CSV, JSON, Excel)

• Recipe Analysis

  • Create recipe data model
  • Implement ingredient parsing
  • Add nutritional calculation for recipes

Phase 4: Documentation and Testing

• Documentation

  • Create comprehensive API documentation
  • Write tutorials and examples
  • Add docstrings to all public methods

• Testing

  • Implement unit tests for all components
  • Add integration tests for Django models
  • Create fixtures for testing

• CI/CD

  • Set up continuous integration
  • Implement automated testing
  • Add code quality checks

Phase 5: Deployment and Maintenance

• Packaging

  • Finalize package structure
  • Create PyPI release
  • Add versioning strategy

• Documentation Hosting

  • Set up ReadTheDocs integration
  • Configure automatic documentation builds
  • Publish documentation online

• Performance Optimization

  • Optimize database queries
  • Implement connection pooling
  • Add performance benchmarks

• Monitoring

  • Add logging throughout the library
  • Implement API usage tracking
  • Create health check endpoints

Future Considerations

• GraphQL API for more flexible querying
• Machine learning integration for food recognition
• Mobile app integration
• Support for additional food databases beyond USDA FDC
• Internationalization and localization