3743ed4ca8
Extract domain value objects and ports from parsing.py, move Docling-specific code to infra/local_converter.py, and convert analysis_service to a class with injected DocumentConverter. This prepares the codebase for plugging in alternative conversion backends (e.g. Docling Serve) via the Protocol pattern. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
51 lines
1.2 KiB
Python
51 lines
1.2 KiB
Python
"""Domain value objects — pure data structures for document conversion.
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These types define the contract between the domain and infrastructure layers.
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They have ZERO external dependencies (no docling, no HTTP, no DB).
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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@dataclass
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class PageElement:
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type: str
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bbox: list[float]
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content: str
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level: int = 0
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@dataclass
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class PageDetail:
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page_number: int
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width: float
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height: float
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elements: list[PageElement] = field(default_factory=list)
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@dataclass
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class ConversionOptions:
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do_ocr: bool = True
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do_table_structure: bool = True
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table_mode: str = "accurate"
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do_code_enrichment: bool = False
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do_formula_enrichment: bool = False
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do_picture_classification: bool = False
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do_picture_description: bool = False
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generate_picture_images: bool = False
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generate_page_images: bool = False
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images_scale: float = 1.0
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def is_default(self) -> bool:
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return self == ConversionOptions()
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@dataclass
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class ConversionResult:
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page_count: int
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content_markdown: str
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content_html: str
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pages: list[PageDetail]
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skipped_items: int = 0
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