coulomb/markitect-main
4
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions
markitect-main/markitect/schema_generator.py

12 lines
353 B
Python
Raw Permalink Normal View History

feat: Complete Issue #5 - Schema Generation Foundation for arc42 Architecture Documentation CRITICAL MILESTONE: Establish schema-driven architecture foundation that unlocks the entire pathway to HolyGrailRequirement - intelligent arc42 architecture documentation with AI-supported plan-actual comparison capabilities. Major Components Implemented: 🎯 SCHEMA GENERATION SERVICE: • SchemaGenerator class with sophisticated AST analysis capabilities • Depth-limited heading extraction for arc42 section-specific schemas • Comprehensive structural element detection (headings, paragraphs, lists, code blocks, etc.) • JSON Schema Draft 7 compliant output with proper validation metadata • Robust error handling with domain-specific exceptions (FileNotFoundError, InvalidDepthError) 🖥️ CLI INTEGRATION: • generate-schema command with full argument and option support • Multiple output formats (JSON, YAML) with stdout or file output • Configurable depth limiting for architectural document analysis • User-friendly summaries and progress feedback • Integration with existing CLI framework and error handling patterns 📊 COMPREHENSIVE TESTING: • 6 comprehensive test scenarios covering core functionality and edge cases • Perfect integration with architectural test system (71 service layer tests passing) • Test coverage for schema generation, depth limiting, error handling, and JSON compliance • Architectural layer L4 (Service) test placement following reverse dependency principles 🏗️ STRATEGIC ARCHITECTURE: • Leverages existing AST processing infrastructure for maximum efficiency • Builds on proven markdown-it parsing with intelligent caching • Seamless integration with existing CLI framework and configuration system • Foundation for Issues #7 (Schema Validation) and #8 (Validation Errors) Technical Excellence: - Full JSON Schema Draft 7 specification compliance for validator compatibility - Sophisticated AST token analysis with structural pattern recognition - Configurable depth filtering essential for arc42 template compliance - Comprehensive metadata extraction for architectural analysis - Robust exception handling with actionable error messages Strategic Value: - 🎯 33% completion of critical path Phase 1 (Schema Foundation) - 🔑 Unlocks schema validation and error reporting capabilities - 🏛️ Essential building block for arc42 architectural documentation intelligence - 🚀 Direct pathway to AI-supported plan-actual comparison capabilities This implementation transforms MarkiTect from advanced markdown processor toward intelligent architecture documentation platform, establishing the schema-driven foundation critical for achieving the HolyGrailRequirement of arc42 compliance with AI intelligence. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-09-29 14:53:05 +02:00
"""
feat(spaces): implement Phase 0-1 of Information Space Service Phase 0 - Project Organization: - Create docs/PROJECT_STRUCTURE.md documenting codebase layout - Create markitect/core/ with parser, serializer, document_manager, workspace - Create markitect/schema/ consolidating 6 schema_*.py modules - Create markitect/storage/ with database module - Maintain backward compatibility via re-exports from original locations - Add docs/roadmap/information-space-service/ with README and WORKPLAN Phase 1 - Foundation (Weeks 1-3): - Week 1: Core domain models (InformationSpace, SpaceDocument, SpaceConfig, SpaceMetadata, SpaceVariable, TransclusionReference, SpaceStatus) - Week 2: Repository layer with interfaces (ISpaceRepository, IDocumentAssociationRepository, IVariableRepository, IReferenceRepository) and SQLite implementations with foreign key cascade deletes - Week 3: SpaceService orchestration layer with full CRUD, document, variable, and reference tracking operations Test coverage: 124 tests (25 model + 63 repository + 36 integration) Capabilities delivered: - CAP-001: InformationSpace entity with lifecycle management - CAP-002: SpaceRepository CRUD with SQLite backing - CAP-003: Document-Space associations with path-based organization - CAP-004: Space metadata and configuration schemas - CAP-005: Database schema with migrations Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-02-08 02:02:46 +01:00
Schema Generator - Backward Compatibility Module.
feat: Complete Issue #5 - Schema Generation Foundation for arc42 Architecture Documentation CRITICAL MILESTONE: Establish schema-driven architecture foundation that unlocks the entire pathway to HolyGrailRequirement - intelligent arc42 architecture documentation with AI-supported plan-actual comparison capabilities. Major Components Implemented: 🎯 SCHEMA GENERATION SERVICE: • SchemaGenerator class with sophisticated AST analysis capabilities • Depth-limited heading extraction for arc42 section-specific schemas • Comprehensive structural element detection (headings, paragraphs, lists, code blocks, etc.) • JSON Schema Draft 7 compliant output with proper validation metadata • Robust error handling with domain-specific exceptions (FileNotFoundError, InvalidDepthError) 🖥️ CLI INTEGRATION: • generate-schema command with full argument and option support • Multiple output formats (JSON, YAML) with stdout or file output • Configurable depth limiting for architectural document analysis • User-friendly summaries and progress feedback • Integration with existing CLI framework and error handling patterns 📊 COMPREHENSIVE TESTING: • 6 comprehensive test scenarios covering core functionality and edge cases • Perfect integration with architectural test system (71 service layer tests passing) • Test coverage for schema generation, depth limiting, error handling, and JSON compliance • Architectural layer L4 (Service) test placement following reverse dependency principles 🏗️ STRATEGIC ARCHITECTURE: • Leverages existing AST processing infrastructure for maximum efficiency • Builds on proven markdown-it parsing with intelligent caching • Seamless integration with existing CLI framework and configuration system • Foundation for Issues #7 (Schema Validation) and #8 (Validation Errors) Technical Excellence: - Full JSON Schema Draft 7 specification compliance for validator compatibility - Sophisticated AST token analysis with structural pattern recognition - Configurable depth filtering essential for arc42 template compliance - Comprehensive metadata extraction for architectural analysis - Robust exception handling with actionable error messages Strategic Value: - 🎯 33% completion of critical path Phase 1 (Schema Foundation) - 🔑 Unlocks schema validation and error reporting capabilities - 🏛️ Essential building block for arc42 architectural documentation intelligence - 🚀 Direct pathway to AI-supported plan-actual comparison capabilities This implementation transforms MarkiTect from advanced markdown processor toward intelligent architecture documentation platform, establishing the schema-driven foundation critical for achieving the HolyGrailRequirement of arc42 compliance with AI intelligence. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-09-29 14:53:05 +02:00
feat(spaces): implement Phase 0-1 of Information Space Service Phase 0 - Project Organization: - Create docs/PROJECT_STRUCTURE.md documenting codebase layout - Create markitect/core/ with parser, serializer, document_manager, workspace - Create markitect/schema/ consolidating 6 schema_*.py modules - Create markitect/storage/ with database module - Maintain backward compatibility via re-exports from original locations - Add docs/roadmap/information-space-service/ with README and WORKPLAN Phase 1 - Foundation (Weeks 1-3): - Week 1: Core domain models (InformationSpace, SpaceDocument, SpaceConfig, SpaceMetadata, SpaceVariable, TransclusionReference, SpaceStatus) - Week 2: Repository layer with interfaces (ISpaceRepository, IDocumentAssociationRepository, IVariableRepository, IReferenceRepository) and SQLite implementations with foreign key cascade deletes - Week 3: SpaceService orchestration layer with full CRUD, document, variable, and reference tracking operations Test coverage: 124 tests (25 model + 63 repository + 36 integration) Capabilities delivered: - CAP-001: InformationSpace entity with lifecycle management - CAP-002: SpaceRepository CRUD with SQLite backing - CAP-003: Document-Space associations with path-based organization - CAP-004: Space metadata and configuration schemas - CAP-005: Database schema with migrations Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-02-08 02:02:46 +01:00
This module re-exports from markitect.schema.generator for backward compatibility.
New code should import from markitect.schema.generator directly.
feat: Complete Issue #5 - Schema Generation Foundation for arc42 Architecture Documentation CRITICAL MILESTONE: Establish schema-driven architecture foundation that unlocks the entire pathway to HolyGrailRequirement - intelligent arc42 architecture documentation with AI-supported plan-actual comparison capabilities. Major Components Implemented: 🎯 SCHEMA GENERATION SERVICE: • SchemaGenerator class with sophisticated AST analysis capabilities • Depth-limited heading extraction for arc42 section-specific schemas • Comprehensive structural element detection (headings, paragraphs, lists, code blocks, etc.) • JSON Schema Draft 7 compliant output with proper validation metadata • Robust error handling with domain-specific exceptions (FileNotFoundError, InvalidDepthError) 🖥️ CLI INTEGRATION: • generate-schema command with full argument and option support • Multiple output formats (JSON, YAML) with stdout or file output • Configurable depth limiting for architectural document analysis • User-friendly summaries and progress feedback • Integration with existing CLI framework and error handling patterns 📊 COMPREHENSIVE TESTING: • 6 comprehensive test scenarios covering core functionality and edge cases • Perfect integration with architectural test system (71 service layer tests passing) • Test coverage for schema generation, depth limiting, error handling, and JSON compliance • Architectural layer L4 (Service) test placement following reverse dependency principles 🏗️ STRATEGIC ARCHITECTURE: • Leverages existing AST processing infrastructure for maximum efficiency • Builds on proven markdown-it parsing with intelligent caching • Seamless integration with existing CLI framework and configuration system • Foundation for Issues #7 (Schema Validation) and #8 (Validation Errors) Technical Excellence: - Full JSON Schema Draft 7 specification compliance for validator compatibility - Sophisticated AST token analysis with structural pattern recognition - Configurable depth filtering essential for arc42 template compliance - Comprehensive metadata extraction for architectural analysis - Robust exception handling with actionable error messages Strategic Value: - 🎯 33% completion of critical path Phase 1 (Schema Foundation) - 🔑 Unlocks schema validation and error reporting capabilities - 🏛️ Essential building block for arc42 architectural documentation intelligence - 🚀 Direct pathway to AI-supported plan-actual comparison capabilities This implementation transforms MarkiTect from advanced markdown processor toward intelligent architecture documentation platform, establishing the schema-driven foundation critical for achieving the HolyGrailRequirement of arc42 compliance with AI intelligence. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-09-29 14:53:05 +02:00
"""
feat(spaces): implement Phase 0-1 of Information Space Service Phase 0 - Project Organization: - Create docs/PROJECT_STRUCTURE.md documenting codebase layout - Create markitect/core/ with parser, serializer, document_manager, workspace - Create markitect/schema/ consolidating 6 schema_*.py modules - Create markitect/storage/ with database module - Maintain backward compatibility via re-exports from original locations - Add docs/roadmap/information-space-service/ with README and WORKPLAN Phase 1 - Foundation (Weeks 1-3): - Week 1: Core domain models (InformationSpace, SpaceDocument, SpaceConfig, SpaceMetadata, SpaceVariable, TransclusionReference, SpaceStatus) - Week 2: Repository layer with interfaces (ISpaceRepository, IDocumentAssociationRepository, IVariableRepository, IReferenceRepository) and SQLite implementations with foreign key cascade deletes - Week 3: SpaceService orchestration layer with full CRUD, document, variable, and reference tracking operations Test coverage: 124 tests (25 model + 63 repository + 36 integration) Capabilities delivered: - CAP-001: InformationSpace entity with lifecycle management - CAP-002: SpaceRepository CRUD with SQLite backing - CAP-003: Document-Space associations with path-based organization - CAP-004: Space metadata and configuration schemas - CAP-005: Database schema with migrations Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-02-08 02:02:46 +01:00
# Re-export from schema package for backward compatibility
from markitect.schema.generator import SchemaGenerator
feat: Complete Issue #5 - Schema Generation Foundation for arc42 Architecture Documentation CRITICAL MILESTONE: Establish schema-driven architecture foundation that unlocks the entire pathway to HolyGrailRequirement - intelligent arc42 architecture documentation with AI-supported plan-actual comparison capabilities. Major Components Implemented: 🎯 SCHEMA GENERATION SERVICE: • SchemaGenerator class with sophisticated AST analysis capabilities • Depth-limited heading extraction for arc42 section-specific schemas • Comprehensive structural element detection (headings, paragraphs, lists, code blocks, etc.) • JSON Schema Draft 7 compliant output with proper validation metadata • Robust error handling with domain-specific exceptions (FileNotFoundError, InvalidDepthError) 🖥️ CLI INTEGRATION: • generate-schema command with full argument and option support • Multiple output formats (JSON, YAML) with stdout or file output • Configurable depth limiting for architectural document analysis • User-friendly summaries and progress feedback • Integration with existing CLI framework and error handling patterns 📊 COMPREHENSIVE TESTING: • 6 comprehensive test scenarios covering core functionality and edge cases • Perfect integration with architectural test system (71 service layer tests passing) • Test coverage for schema generation, depth limiting, error handling, and JSON compliance • Architectural layer L4 (Service) test placement following reverse dependency principles 🏗️ STRATEGIC ARCHITECTURE: • Leverages existing AST processing infrastructure for maximum efficiency • Builds on proven markdown-it parsing with intelligent caching • Seamless integration with existing CLI framework and configuration system • Foundation for Issues #7 (Schema Validation) and #8 (Validation Errors) Technical Excellence: - Full JSON Schema Draft 7 specification compliance for validator compatibility - Sophisticated AST token analysis with structural pattern recognition - Configurable depth filtering essential for arc42 template compliance - Comprehensive metadata extraction for architectural analysis - Robust exception handling with actionable error messages Strategic Value: - 🎯 33% completion of critical path Phase 1 (Schema Foundation) - 🔑 Unlocks schema validation and error reporting capabilities - 🏛️ Essential building block for arc42 architectural documentation intelligence - 🚀 Direct pathway to AI-supported plan-actual comparison capabilities This implementation transforms MarkiTect from advanced markdown processor toward intelligent architecture documentation platform, establishing the schema-driven foundation critical for achieving the HolyGrailRequirement of arc42 compliance with AI intelligence. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-09-29 14:53:05 +02:00
feat(spaces): implement Phase 0-1 of Information Space Service Phase 0 - Project Organization: - Create docs/PROJECT_STRUCTURE.md documenting codebase layout - Create markitect/core/ with parser, serializer, document_manager, workspace - Create markitect/schema/ consolidating 6 schema_*.py modules - Create markitect/storage/ with database module - Maintain backward compatibility via re-exports from original locations - Add docs/roadmap/information-space-service/ with README and WORKPLAN Phase 1 - Foundation (Weeks 1-3): - Week 1: Core domain models (InformationSpace, SpaceDocument, SpaceConfig, SpaceMetadata, SpaceVariable, TransclusionReference, SpaceStatus) - Week 2: Repository layer with interfaces (ISpaceRepository, IDocumentAssociationRepository, IVariableRepository, IReferenceRepository) and SQLite implementations with foreign key cascade deletes - Week 3: SpaceService orchestration layer with full CRUD, document, variable, and reference tracking operations Test coverage: 124 tests (25 model + 63 repository + 36 integration) Capabilities delivered: - CAP-001: InformationSpace entity with lifecycle management - CAP-002: SpaceRepository CRUD with SQLite backing - CAP-003: Document-Space associations with path-based organization - CAP-004: Space metadata and configuration schemas - CAP-005: Database schema with migrations Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-02-08 02:02:46 +01:00
__all__ = ['SchemaGenerator']
Reference in a new issue Copy permalink