tegwick
9b12875681
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>
48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
"""
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Markdown AST Parser.
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This module provides functionality to parse markdown content into an
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Abstract Syntax Tree (AST) using the markdown-it library.
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"""
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from markdown_it import MarkdownIt
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def parse_markdown_to_ast(md_content: str):
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"""
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Parse markdown content into a JSON-serializable AST.
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Args:
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md_content: Markdown text to parse
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Returns:
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List of token dictionaries representing the AST
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Example:
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ast = parse_markdown_to_ast("# Hello\\n\\nWorld")
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"""
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# Enable table parsing and other common plugins
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md = MarkdownIt("commonmark", {"tables": True}).enable(['table'])
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tokens = md.parse(md_content)
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# Convert to a JSON-serializable list of dicts
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def token_to_dict(token):
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d = {
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'type': token.type,
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'tag': token.tag,
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'attrs': token.attrs,
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'map': token.map,
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'nesting': token.nesting,
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'level': token.level,
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'children': [token_to_dict(child) if child else None for child in token.children] if token.children else None,
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'content': token.content,
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'markup': token.markup,
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'info': token.info,
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'meta': token.meta,
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'block': token.block,
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'hidden': token.hidden
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}
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return {k: v for k, v in d.items() if v is not None} # Remove None values
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return [token_to_dict(token) for token in tokens]
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