Implements plugin-based content filtering system with multi-level caching:
Core Components:
- FilterEngine: Main orchestrator for content filtering
- FilterCache: 3-level caching (memory, AI results, filterset results)
- FilterConfig: Configuration loader for filter_config.json & filtersets.json
- FilterResult & AIAnalysisResult: Data models for filter results
Architecture:
- BaseStage: Abstract class for pipeline stages
- BaseFilterPlugin: Abstract class for filter plugins
- Multi-threaded parallel processing support
- Content-hash based AI result caching (cost savings)
- Filterset result caching (fast filterset switching)
Configuration:
- filter_config.json: AI models, caching, parallel workers
- Using only Llama 70B for cost efficiency
- Compatible with existing filtersets.json
Integration:
- apply_filterset() API compatible with user preferences
- process_batch() for batch post processing
- Lazy-loaded stages to avoid import errors when AI disabled
Related to issue #8 (filtering engine implementation)
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Co-Authored-By: Claude <noreply@anthropic.com>
chelsea
94e12041ec