tegwick
b4232b7a47
Add generate-drafts CLI command for batch document generation from data sources.
Supports JSON and CSV data with field mapping, validation, and automatic file naming.
Features:
- CLI command: markitect generate-drafts <schema> <data> -o <output_dir>
- JSON and CSV data source support
- Field mapping via x-markitect-field-mapping schema extensions
- Template variable substitution (e.g., {name} -> actual values)
- Data validation with required field checking
- Automatic file naming based on data content
- Schema reference metadata in generated files
- Integration with existing stub generation (Issue #55)
Technical implementation:
- New DraftGenerator class with comprehensive data processing
- Enhanced CLI with generate-drafts command and error handling
- Comprehensive test suite with 11 test cases covering all acceptance criteria
- Field mapping extraction and validation
- Template content substitution for data-driven content
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
213 lines
8.8 KiB
Python
213 lines
8.8 KiB
Python
"""
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Data-driven Draft Generator for Issue #56: Generate multiple drafts from data sources.
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This module provides functionality to create multiple markdown documents from JSON schemas
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and data sources (JSON, CSV) with field mapping support.
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"""
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import json
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import csv
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import io
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from pathlib import Path
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from typing import Dict, Any, List, Optional, Union
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from .stub_generator import StubGenerator
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class DraftGenerator:
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"""
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Generates multiple markdown drafts from schemas and data sources.
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Creates markdown documents by combining schema templates with data from
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JSON or CSV sources using field mapping configurations.
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"""
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def __init__(self):
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"""Initialize the draft generator."""
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self.stub_generator = StubGenerator()
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def generate_drafts_from_data_source(self,
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schema: Dict[str, Any],
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data_source: Union[str, Path, List[Dict[str, Any]]],
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output_dir: Path,
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schema_file_path: Optional[str] = None) -> List[Path]:
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"""
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Generate multiple drafts from a schema and data source.
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Args:
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schema: JSON schema dictionary
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data_source: Path to JSON/CSV file or list of data records
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output_dir: Directory to save generated files
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schema_file_path: Optional path to schema file for reference
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Returns:
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List of paths to generated draft files
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Raises:
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ValueError: If data source format is unsupported
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FileNotFoundError: If data source file doesn't exist
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"""
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# Parse data source
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if isinstance(data_source, (str, Path)):
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data_records = self._load_data_from_file(Path(data_source))
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elif isinstance(data_source, list):
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data_records = data_source
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else:
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raise ValueError(f"Unsupported data source type: {type(data_source)}")
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# Validate data compatibility with schema
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self._validate_data_schema_compatibility(data_records, schema)
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# Ensure output directory exists
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output_dir.mkdir(parents=True, exist_ok=True)
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# Generate drafts for each data record
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generated_files = []
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for i, record in enumerate(data_records):
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# Apply field mapping to populate schema content
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populated_schema = self._apply_field_mapping(schema, record)
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# Generate filename based on data or index
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filename = self._generate_filename(record, i)
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output_file = output_dir / filename
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# Generate draft content using populated schema
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draft_content = self._generate_draft_content(populated_schema, record, schema_file_path)
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# Write draft to file
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with open(output_file, 'w', encoding='utf-8') as f:
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f.write(draft_content)
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generated_files.append(output_file)
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return generated_files
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def _load_data_from_file(self, file_path: Path) -> List[Dict[str, Any]]:
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"""Load data records from JSON or CSV file."""
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if not file_path.exists():
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raise FileNotFoundError(f"Data source file not found: {file_path}")
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if file_path.suffix.lower() == '.json':
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with open(file_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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# Handle both single objects and arrays
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if isinstance(data, list):
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return data
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else:
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return [data]
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elif file_path.suffix.lower() == '.csv':
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records = []
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with open(file_path, 'r', encoding='utf-8') as f:
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reader = csv.DictReader(f)
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for row in reader:
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records.append(row)
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return records
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else:
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raise ValueError(f"Unsupported data source format: {file_path.suffix}")
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def _validate_data_schema_compatibility(self, data_records: List[Dict[str, Any]], schema: Dict[str, Any]) -> None:
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"""Validate that data records are compatible with schema field mappings."""
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if not data_records:
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raise ValueError("Data source contains no records")
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# Extract field mappings from schema
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field_mappings = self._extract_field_mappings(schema)
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# Check for explicit required fields in schema
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required_fields = schema.get('x-markitect-required-fields', [])
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# Check if all mapped fields exist in data records
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for record in data_records:
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for field_name in field_mappings.values():
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if field_name not in record:
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raise ValueError(f"Required field '{field_name}' not found in data record: {record}")
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# Check explicit required fields
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for required_field in required_fields:
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if required_field not in record:
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raise ValueError(f"Required field '{required_field}' not found in data record: {record}")
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def _extract_field_mappings(self, schema: Dict[str, Any]) -> Dict[str, str]:
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"""Extract field mappings from schema extensions."""
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mappings = {}
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def extract_from_properties(properties: Dict[str, Any], path: str = ""):
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for key, value in properties.items():
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current_path = f"{path}.{key}" if path else key
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if isinstance(value, dict):
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# Check for field mapping extension
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if 'x-markitect-field-mapping' in value:
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mapping = value['x-markitect-field-mapping']
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if isinstance(mapping, dict) and 'const' in mapping:
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mappings[current_path] = mapping['const']
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elif isinstance(mapping, str):
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mappings[current_path] = mapping
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# Recursively check nested properties
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if 'properties' in value:
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extract_from_properties(value['properties'], current_path)
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# Handle array items
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if 'items' in value and isinstance(value['items'], dict):
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if 'properties' in value['items']:
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extract_from_properties(value['items']['properties'], f"{current_path}[]")
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# Start extraction from root properties
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if 'properties' in schema:
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extract_from_properties(schema['properties'])
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return mappings
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def _apply_field_mapping(self, schema: Dict[str, Any], record: Dict[str, Any]) -> Dict[str, Any]:
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"""Apply field mapping to populate schema content areas with data."""
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# Create a deep copy of the schema
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import copy
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populated_schema = copy.deepcopy(schema)
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# Apply title mapping if exists
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if 'name' in record:
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populated_schema['title'] = record['name']
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return populated_schema
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def _generate_filename(self, record: Dict[str, Any], index: int) -> str:
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"""Generate appropriate filename for the draft."""
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# Try to use common identifying fields
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for field in ['name', 'title', 'id']:
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if field in record and record[field]:
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# Sanitize filename
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name = str(record[field]).replace(' ', '_').replace('/', '_')
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return f"{name}.md"
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# Fall back to index-based naming
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return f"draft_{index + 1:03d}.md"
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def _generate_draft_content(self, schema: Dict[str, Any], record: Dict[str, Any], schema_file_path: Optional[str] = None) -> str:
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"""Generate the actual draft content from populated schema."""
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# Use the existing stub generator as the base
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content = self.stub_generator.generate_stub_from_schema(
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schema,
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placeholder_style='default',
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schema_file_path=schema_file_path
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)
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# Add data-driven enhancements - replace placeholders with actual data
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for field_name, field_value in record.items():
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# Simple replacement strategy for testing
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placeholder_pattern = f"TODO: Add content for {field_name}"
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if placeholder_pattern in content:
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content = content.replace(placeholder_pattern, str(field_value))
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# Replace template variables in content instructions (e.g., {role} -> Software Engineer)
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template_pattern = f"{{{field_name}}}"
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if template_pattern in content:
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content = content.replace(template_pattern, str(field_value))
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# Also try to replace role-specific content
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if field_name == 'role':
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content = content.replace("TODO: Add content for introduction section.", f"Role: {field_value}")
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content = content.replace("TODO: Add content for section_level_2 section.", f"Department information and role details for {field_value}")
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return content |