- Add 5 new Knowledge Graph Quality Assurance modules
- Implement solutions for LinkedIn post problems (templates, seed data, deduplication, conflicts)
- Add agentic analytics capabilities and autonomous AI features
- Emphasize open source and free nature throughout README
- Add comprehensive use cases and enterprise features
- Update module count to 20 modules with 120+ submodules
- Add GraphRAG integration and SLM support
- Include critical enterprise challenges and solutions
- Added detailed function tables for all 22 modules
- Documented 450+ functions with parameters and return types
- Included function statistics and categorization
- Enhanced Modules.md with complete function reference
- Organized functions by module for easy navigation
- Complete module reference with all 22 main modules
- Detailed submodule breakdown with 140+ submodules
- Comprehensive import statements for all modules
- Practical code examples for each module and submodule
- Complete function reference with usage examples
- Module dependency information
- Quick start examples and common use cases
- Complete module index with statistics
- Updated all package references from semanticore to semantica
- Updated all class names from SemantiCore to Semantica
- Renamed logo files: SemantiCore Logo.png -> Semantica Logo.png
- Renamed logo files: SemantiCore Logo Dark.png -> Semantica Logo Dark.png
- Renamed roadmap file: SemantiCore_Development_Roadmap.md -> Semantica_Development_Roadmap.md
- Updated all URLs, links, and documentation references
- Updated all code examples and installation instructions
- Maintained all functionality while updating branding
- Add template_manager to enforce fixed templates and prevent AI invention
- Add seed_manager to initialize KG with existing known data
- Add semantic_deduplicator to merge semantically similar entities
- Add conflict_detector to flag source disagreements
- Add provenance_tracker for full source transparency
- Update roadmap with Q5-Q6 milestones for KG quality features
- Include new API examples and design principles
- Address LinkedIn post challenges: fixed templates, seed data, deduplication, conflict detection
- Add detailed requirements document with 10 major requirements covering universal data processing, semantic intelligence, knowledge graphs, vector embeddings, real-time streaming, multi-language support, enterprise scalability, APIs, quality assurance, and security compliance
- Add comprehensive design document with modular microservices architecture, component interfaces, data models, error handling, testing strategy, and security design
- Add detailed implementation plan with 15 major tasks and 31 sub-tasks covering complete development roadmap from foundation to production deployment
- Specification aims to build open source semantic layer platform that surpasses Diffbot capabilities while remaining completely free
- Add complete Sphinx documentation structure
- Include API reference, tutorials, and examples
- Add GitHub Actions workflow for auto-deployment
- Include custom CSS and JavaScript for enhanced UI
- Add Makefile with build commands
- Include requirements for documentation dependencies
- Add quick start tutorial and getting started guide
- Configure Read the Docs theme with custom styling
- Add spell checking, link checking, and quality tools
- Set up automatic deployment to GitHub Pages
Documentation includes:
- Getting started guide with installation and basic usage
- Comprehensive examples for all major features
- Complete API reference with type hints
- Tutorials for different use cases
- Custom styling with SemantiCore branding
- Mobile-responsive design
- Dark mode support
- Performance optimization guides
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