tegwick
0004fa2a0f
This implementation adds comprehensive support for content field instructions that provide guidance for document generation from schemas. ## Key Features Added: ### CLI Options - `--include-content-instructions` flag to enable content instruction fields - `--instruction-type` parameter with options: description, example, constraint, template - Full integration with existing outline mode and heading text capture features ### Schema Generation Enhancements - Content instruction fields (x-markitect-content-instructions) with contextual guidance text - Instruction type metadata (x-markitect-instruction-type) for type specification - Metaschema extension (x-markitect-content-instructions-enabled) for feature detection - Support for headings, paragraphs, and lists content instructions ### Error Handling - InvalidInstructionTypeError for robust validation of instruction type parameters - Comprehensive input validation with clear error messages ### Integration and Compatibility - Seamless integration with outline mode and heading text capture - Full backward compatibility - existing behavior unchanged when feature disabled - Works with all existing CLI options and modes ### Documentation - Updated CLI help with examples and detailed feature descriptions - Clear documentation of all instruction types and their purposes ## Technical Implementation: - Enhanced SchemaGenerator with content instruction generation logic - Added `_generate_content_instruction` method for contextual instruction text - Extended schema structure to include instruction metadata - Maintained clean separation of concerns and existing code patterns ## Testing and Validation: - Comprehensive test coverage following TDD8 methodology - All existing functionality preserved and tested - Integration tests for all feature combinations - Error handling and edge case validation This completes Issue #54 with full feature implementation, documentation, and comprehensive testing coverage. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
466 lines
19 KiB
Python
466 lines
19 KiB
Python
"""
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Schema Generator for Issue #5: Generate a Schema from a Markdown File.
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This module provides functionality to analyze markdown AST structures and generate
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JSON schemas that describe the document's structural elements with configurable
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depth limitations for architectural documentation analysis.
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"""
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import json
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List, Any, Optional, Set
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from .parser import parse_markdown_to_ast
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from .exceptions import FileNotFoundError, InvalidDepthError, InvalidInstructionTypeError
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class SchemaGenerator:
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"""
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Generates JSON schemas from markdown file AST structures.
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Analyzes the structural elements of markdown documents and creates
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JSON schemas that can be used for validation and compliance checking
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in architecture documentation workflows.
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"""
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def __init__(self):
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"""Initialize the schema generator."""
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self.default_schema_url = "http://json-schema.org/draft-07/schema#"
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def generate_schema_from_file(
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self,
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file_path: Path,
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max_depth: Optional[int] = None,
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mode: Optional[str] = None,
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outline_depth: Optional[int] = None,
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capture_heading_text: bool = False,
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include_content_instructions: bool = False,
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instruction_type: str = 'description'
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) -> Dict[str, Any]:
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"""
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Generate a JSON schema from a markdown file's AST structure.
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Args:
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file_path: Path to the markdown file
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max_depth: Maximum heading depth to include (None = unlimited)
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mode: Generation mode ('outline' for structure-focused schemas)
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outline_depth: Depth limit for outline mode
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capture_heading_text: Whether to capture exact heading text as constraints
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include_content_instructions: Whether to include content instruction fields
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instruction_type: Type of content instructions ('description', 'example', 'constraint', 'template')
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Returns:
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JSON schema as a dictionary
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Raises:
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FileNotFoundError: If the markdown file doesn't exist
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InvalidDepthError: If max_depth is invalid (< 1)
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"""
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# Validate inputs
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if not file_path.exists():
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raise FileNotFoundError(f"Markdown file not found: {file_path}")
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if max_depth is not None and max_depth < 1:
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raise InvalidDepthError(f"max_depth must be >= 1, got: {max_depth}")
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# Validate instruction type
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valid_instruction_types = {'description', 'example', 'constraint', 'template'}
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if instruction_type not in valid_instruction_types:
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raise InvalidInstructionTypeError(f"Invalid instruction type '{instruction_type}'. Must be one of: {', '.join(valid_instruction_types)}")
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# Read and parse the markdown file
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content = file_path.read_text(encoding='utf-8')
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ast_tokens = parse_markdown_to_ast(content)
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# Analyze the AST structure
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structure_analysis = self._analyze_ast_structure(ast_tokens, max_depth)
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# Generate the JSON schema
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schema = self._create_json_schema(
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structure_analysis,
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file_path.name,
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mode=mode,
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outline_depth=outline_depth,
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capture_heading_text=capture_heading_text,
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include_content_instructions=include_content_instructions,
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instruction_type=instruction_type
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)
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return schema
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def _analyze_ast_structure(self, tokens: List[Dict[str, Any]], max_depth: Optional[int]) -> Dict[str, Any]:
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"""
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Analyze AST tokens to extract structural patterns.
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Args:
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tokens: List of AST tokens from markdown-it
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max_depth: Maximum heading depth to analyze
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Returns:
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Dictionary containing structural analysis
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"""
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analysis = {
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'headings': defaultdict(list),
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'paragraphs': [],
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'lists': [],
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'code_blocks': [],
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'blockquotes': [],
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'tables': [],
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'links': [],
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'images': [],
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'emphasis': [],
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'structure_types': set()
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}
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current_heading_level = 0
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i = 0
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while i < len(tokens):
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token = tokens[i]
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token_type = token.get('type', '')
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# Track all structural types found
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analysis['structure_types'].add(token_type)
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# Analyze headings with depth filtering
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if token_type == 'heading_open':
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level = self._extract_heading_level(token.get('tag', ''))
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if max_depth is None or level <= max_depth:
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heading_content = self._extract_heading_content(tokens, i)
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analysis['headings'][f'level_{level}'].append({
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'content': heading_content,
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'level': level,
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'position': i
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})
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current_heading_level = level
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# Analyze paragraphs
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elif token_type == 'paragraph_open':
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paragraph_content = self._extract_paragraph_content(tokens, i)
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analysis['paragraphs'].append({
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'content': paragraph_content,
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'position': i,
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'under_heading_level': current_heading_level
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})
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# Analyze lists
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elif token_type in ['bullet_list_open', 'ordered_list_open']:
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list_structure = self._extract_list_structure(tokens, i)
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analysis['lists'].append({
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'type': 'bullet' if token_type == 'bullet_list_open' else 'ordered',
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'structure': list_structure,
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'position': i,
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'under_heading_level': current_heading_level
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})
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# Analyze code blocks
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elif token_type == 'code_block' or token_type == 'fence':
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code_info = self._extract_code_block_info(token)
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analysis['code_blocks'].append({
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'language': code_info.get('language', ''),
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'content_length': len(code_info.get('content', '')),
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'position': i,
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'under_heading_level': current_heading_level
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})
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# Analyze blockquotes
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elif token_type == 'blockquote_open':
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quote_content = self._extract_blockquote_content(tokens, i)
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analysis['blockquotes'].append({
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'content': quote_content,
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'position': i,
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'under_heading_level': current_heading_level
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})
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# Analyze tables
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elif token_type == 'table_open':
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table_structure = self._extract_table_structure(tokens, i)
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analysis['tables'].append({
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'columns': table_structure.get('columns', 0),
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'rows': table_structure.get('rows', 0),
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'position': i,
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'under_heading_level': current_heading_level
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})
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# Analyze inline elements
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elif token_type == 'inline':
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inline_analysis = self._analyze_inline_content(token)
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analysis['links'].extend(inline_analysis.get('links', []))
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analysis['images'].extend(inline_analysis.get('images', []))
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analysis['emphasis'].extend(inline_analysis.get('emphasis', []))
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i += 1
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# Convert sets to lists for JSON serialization
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analysis['structure_types'] = list(analysis['structure_types'])
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return analysis
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def _create_json_schema(
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self,
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analysis: Dict[str, Any],
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filename: str,
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mode: Optional[str] = None,
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outline_depth: Optional[int] = None,
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capture_heading_text: bool = False,
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include_content_instructions: bool = False,
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instruction_type: str = 'description'
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) -> Dict[str, Any]:
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"""
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Create a JSON schema from structural analysis.
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Args:
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analysis: Structural analysis of the document
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filename: Name of the source file
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mode: Generation mode ('outline' for structure-focused schemas)
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outline_depth: Depth limit for outline mode
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capture_heading_text: Whether to capture exact heading text as constraints
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include_content_instructions: Whether to include content instruction fields
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instruction_type: Type of content instructions to generate
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Returns:
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JSON schema dictionary
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"""
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# Determine title format based on mode
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title_preposition = "from" if mode == "outline" else "for"
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schema = {
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"$schema": self.default_schema_url,
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"type": "object",
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"title": f"Schema {title_preposition} {filename}",
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"description": f"JSON schema describing the structure of {filename}",
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"properties": {}
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}
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# Add metaschema extensions for outline mode
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if mode == "outline":
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schema["x-markitect-outline-mode"] = True
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if outline_depth is not None:
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schema["x-markitect-outline-depth"] = outline_depth
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# Add metaschema extension for heading text capture
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if capture_heading_text:
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schema["x-markitect-heading-text-capture"] = True
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# Add metaschema extension for content instructions
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if include_content_instructions:
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schema["x-markitect-content-instructions-enabled"] = True
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# Add heading structure
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if analysis['headings']:
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heading_properties = {}
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for level_key, headings in analysis['headings'].items():
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if headings: # Only include levels that have content
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# Configure content property based on heading text capture
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if capture_heading_text:
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# Extract actual heading texts in document order
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heading_texts = [heading['content'] for heading in headings]
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content_property = {"enum": heading_texts}
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else:
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content_property = {"type": "string"}
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# Build properties for the heading item
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item_properties = {
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"content": content_property,
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"level": {"type": "integer"},
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"position": {"type": "integer"}
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}
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# Add content instruction fields if enabled
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if include_content_instructions:
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# Generate appropriate instruction text based on heading level
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level_num = int(level_key.split('_')[1])
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section_name = f"level {level_num} heading"
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instruction_text = self._generate_content_instruction(section_name, instruction_type)
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item_properties["x-markitect-content-instructions"] = {
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"type": "string",
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"const": instruction_text
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}
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item_properties["x-markitect-instruction-type"] = {
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"type": "string",
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"enum": [instruction_type]
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}
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heading_properties[level_key] = {
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"type": "array",
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"description": f"Headings at {level_key.replace('_', ' ')}",
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"items": {
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"type": "object",
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"properties": item_properties,
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"required": ["content", "level"]
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},
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"minItems": len(headings),
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"maxItems": len(headings)
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}
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if heading_properties:
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schema["properties"]["headings"] = {
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"type": "object",
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"description": "Document heading structure",
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"properties": heading_properties
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}
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# Add other structural elements
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structural_elements = {
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"paragraphs": ("Text paragraphs", analysis['paragraphs']),
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"lists": ("Lists (ordered and unordered)", analysis['lists']),
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"code_blocks": ("Code blocks and fenced code", analysis['code_blocks']),
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"blockquotes": ("Block quotations", analysis['blockquotes']),
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"tables": ("Tables with rows and columns", analysis['tables']),
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"links": ("Links to external resources", analysis['links']),
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"images": ("Embedded images", analysis['images']),
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"emphasis": ("Text emphasis (bold, italic)", analysis['emphasis'])
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}
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for element_name, (description, element_list) in structural_elements.items():
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if element_list:
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# Build base schema for the element
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element_schema = {
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"type": "array",
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"description": description,
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"minItems": len(element_list),
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"maxItems": len(element_list)
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}
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# Add content instructions for paragraphs and lists if enabled
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if include_content_instructions and element_name in ["paragraphs", "lists"]:
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element_schema["items"] = {
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"type": "object",
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"properties": {
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"content": {"type": "string"},
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"x-markitect-content-instructions": {
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"type": "string",
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"const": self._generate_content_instruction(element_name, instruction_type)
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},
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"x-markitect-instruction-type": {
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"type": "string",
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"enum": [instruction_type]
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}
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}
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}
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schema["properties"][element_name] = element_schema
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# Add metadata
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schema["properties"]["metadata"] = {
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"type": "object",
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"description": "Document structure metadata",
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"properties": {
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"total_elements": {
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"type": "integer",
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"const": sum(len(v) if isinstance(v, list) else 0 for v in analysis.values())
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},
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"structure_types": {
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"type": "array",
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"items": {"type": "string"},
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"description": "All structural element types found",
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"const": analysis['structure_types']
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}
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}
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}
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return schema
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def _extract_heading_level(self, tag: str) -> int:
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"""Extract heading level from HTML tag (h1, h2, etc.)."""
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if tag.startswith('h') and len(tag) == 2:
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try:
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return int(tag[1])
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except ValueError:
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pass
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return 1
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def _extract_heading_content(self, tokens: List[Dict[str, Any]], start_index: int) -> str:
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"""Extract text content from heading tokens."""
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# Look for the inline token that contains the heading text
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for i in range(start_index, min(start_index + 3, len(tokens))):
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token = tokens[i]
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if token.get('type') == 'inline':
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return token.get('content', '')
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return ''
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def _extract_paragraph_content(self, tokens: List[Dict[str, Any]], start_index: int) -> str:
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"""Extract text content from paragraph tokens."""
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# Look for the inline token that contains the paragraph text
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for i in range(start_index, min(start_index + 3, len(tokens))):
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token = tokens[i]
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if token.get('type') == 'inline':
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return token.get('content', '')
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return ''
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def _extract_list_structure(self, tokens: List[Dict[str, Any]], start_index: int) -> Dict[str, Any]:
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"""Extract list structure information."""
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# This is a simplified implementation
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# In a full implementation, we'd parse the nested list structure
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return {
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"type": "list",
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"estimated_items": 1 # Placeholder - would need more complex parsing
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}
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def _extract_code_block_info(self, token: Dict[str, Any]) -> Dict[str, Any]:
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"""Extract code block information."""
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return {
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"language": token.get('info', '').split()[0] if token.get('info') else '',
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"content": token.get('content', '')
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}
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def _extract_blockquote_content(self, tokens: List[Dict[str, Any]], start_index: int) -> str:
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"""Extract blockquote content."""
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# Simplified implementation
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return "blockquote content"
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def _extract_table_structure(self, tokens: List[Dict[str, Any]], start_index: int) -> Dict[str, Any]:
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"""Extract table structure information."""
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# Simplified implementation
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return {
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"columns": 2, # Placeholder
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"rows": 1 # Placeholder
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}
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def _analyze_inline_content(self, token: Dict[str, Any]) -> Dict[str, List[Any]]:
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"""Analyze inline content for links, images, emphasis."""
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result = {
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"links": [],
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"images": [],
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"emphasis": []
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}
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# Analyze children tokens if they exist
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children = token.get('children', [])
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for child in children:
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if child and isinstance(child, dict):
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child_type = child.get('type', '')
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if child_type == 'link_open':
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result['links'].append({"type": "link"})
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elif child_type == 'image':
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result['images'].append({"type": "image"})
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elif child_type in ['em_open', 'strong_open']:
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result['emphasis'].append({"type": child_type})
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return result
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def _generate_content_instruction(self, heading_text: str, instruction_type: str) -> str:
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"""
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Generate appropriate content instruction text based on heading and instruction type.
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Args:
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heading_text: The text of the heading
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instruction_type: Type of instruction to generate
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Returns:
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Instruction text for the content field
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"""
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if instruction_type == "description":
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return f"Provide content for the '{heading_text}' section"
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elif instruction_type == "example":
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return f"Example content for the '{heading_text}' section"
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elif instruction_type == "constraint":
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return f"Content must be relevant to '{heading_text}'"
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elif instruction_type == "template":
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return f"Template content for '{heading_text}' section"
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else:
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# Default fallback
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return f"Content for the '{heading_text}' section" |