221 lines
7.8 KiB
Python
221 lines
7.8 KiB
Python
"""Local docling converter implementation."""
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import asyncio
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from pathlib import Path
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from typing import TYPE_CHECKING, ClassVar, cast
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from haiku.rag.config import AppConfig
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from haiku.rag.converters.base import DocumentConverter
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from haiku.rag.converters.text_utils import TextFileHandler
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if TYPE_CHECKING:
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from docling_core.types.doc.document import DoclingDocument
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from haiku.rag.config.models import ConversionOptions, ModelConfig
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class DoclingLocalConverter(DocumentConverter):
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"""Converter that uses local docling for document conversion.
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This converter runs docling locally in-process to convert documents.
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It handles various document formats including PDF, DOCX, HTML, and plain text.
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"""
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# Extensions supported by docling
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docling_extensions: ClassVar[list[str]] = [
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".adoc",
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".asc",
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".asciidoc",
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".bmp",
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".csv",
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".docx",
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".html",
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".xhtml",
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".jpeg",
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".jpg",
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".latex",
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".md",
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".pdf",
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".png",
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".pptx",
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".qmd",
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".rmd",
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".tex",
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".tiff",
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".xlsx",
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".xml",
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".webp",
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]
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def __init__(self, config: AppConfig):
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"""Initialize the converter with configuration.
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Args:
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config: Application configuration containing conversion options.
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"""
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self.config = config
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@property
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def supported_extensions(self) -> list[str]:
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"""Return list of file extensions supported by this converter."""
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return self.docling_extensions + TextFileHandler.text_extensions
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def _get_vlm_api_url(self, model: "ModelConfig") -> str:
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"""Construct VLM API URL from model config."""
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if model.base_url:
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base = model.base_url.rstrip("/")
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return f"{base}/v1/chat/completions"
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if model.provider == "ollama":
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base = self.config.providers.ollama.base_url.rstrip("/")
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return f"{base}/v1/chat/completions"
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if model.provider == "openai":
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return "https://api.openai.com/v1/chat/completions"
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raise ValueError(f"Unsupported VLM provider: {model.provider}")
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def _get_ocr_options(self, opts: "ConversionOptions"):
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"""Get OCR options based on configuration."""
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from docling.datamodel.pipeline_options import (
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EasyOcrOptions,
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OcrAutoOptions,
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OcrMacOptions,
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RapidOcrOptions,
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TesseractCliOcrOptions,
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TesseractOcrOptions,
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)
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force_ocr = opts.force_ocr
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lang = opts.ocr_lang if opts.ocr_lang else []
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match opts.ocr_engine:
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case "easyocr":
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return EasyOcrOptions(force_full_page_ocr=force_ocr, lang=lang)
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case "rapidocr":
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return RapidOcrOptions(force_full_page_ocr=force_ocr, lang=lang)
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case "tesseract":
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return TesseractOcrOptions(force_full_page_ocr=force_ocr, lang=lang)
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case "tesserocr":
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return TesseractCliOcrOptions(force_full_page_ocr=force_ocr, lang=lang)
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case "ocrmac":
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return OcrMacOptions(force_full_page_ocr=force_ocr, lang=lang)
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case _: # "auto" or any other value
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return OcrAutoOptions(force_full_page_ocr=force_ocr, lang=lang)
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def _sync_convert_docling_file(self, path: Path) -> "DoclingDocument":
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"""Synchronous conversion of docling-supported files."""
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from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
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from docling.datamodel.base_models import InputFormat
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from docling.datamodel.pipeline_options import (
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PdfPipelineOptions,
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PictureDescriptionApiOptions,
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TableFormerMode,
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TableStructureOptions,
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)
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from docling.document_converter import (
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DocumentConverter as DoclingDocConverter,
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)
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from docling.document_converter import (
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FormatOption,
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PdfFormatOption,
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)
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opts = self.config.processing.conversion_options
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pic_desc = opts.picture_description
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pipeline_options = PdfPipelineOptions(
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do_ocr=opts.do_ocr,
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do_table_structure=opts.do_table_structure,
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images_scale=opts.images_scale,
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generate_page_images=opts.generate_page_images,
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generate_picture_images=opts.generate_picture_images or pic_desc.enabled,
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table_structure_options=TableStructureOptions(
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do_cell_matching=opts.table_cell_matching,
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mode=(
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TableFormerMode.FAST
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if opts.table_mode == "fast"
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else TableFormerMode.ACCURATE
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),
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),
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ocr_options=self._get_ocr_options(opts),
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do_picture_description=pic_desc.enabled,
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)
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if pic_desc.enabled:
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from pydantic import AnyUrl
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prompt = self.config.prompts.picture_description
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pipeline_options.enable_remote_services = True
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pipeline_options.picture_description_options = PictureDescriptionApiOptions(
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url=AnyUrl(self._get_vlm_api_url(pic_desc.model)),
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params=dict(
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model=pic_desc.model.name,
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max_completion_tokens=pic_desc.max_tokens,
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),
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prompt=prompt,
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timeout=pic_desc.timeout,
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)
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format_options = cast(
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dict[InputFormat, FormatOption],
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{
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InputFormat.PDF: PdfFormatOption(
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pipeline_options=pipeline_options,
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backend=DoclingParseDocumentBackend,
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)
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},
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)
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converter = DoclingDocConverter(format_options=format_options)
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result = converter.convert(path)
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return result.document
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async def convert_file(self, path: Path) -> "DoclingDocument":
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"""Convert a file to DoclingDocument using local docling.
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Args:
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path: Path to the file to convert.
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Returns:
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DoclingDocument representation of the file.
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Raises:
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ValueError: If the file cannot be converted.
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"""
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try:
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file_extension = path.suffix.lower()
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if file_extension in self.docling_extensions:
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return await asyncio.to_thread(self._sync_convert_docling_file, path)
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elif file_extension in TextFileHandler.text_extensions:
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content = await asyncio.to_thread(path.read_text, encoding="utf-8")
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prepared_content = TextFileHandler.prepare_text_content(
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content, file_extension
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)
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return await self.convert_text(prepared_content, name=f"{path.stem}.md")
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else:
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content = await asyncio.to_thread(path.read_text, encoding="utf-8")
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return await self.convert_text(content, name=f"{path.stem}.md")
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except Exception:
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raise ValueError(f"Failed to parse file: {path}")
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async def convert_text(
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self, text: str, name: str = "content.md", format: str = "md"
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) -> "DoclingDocument":
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"""Convert text content to DoclingDocument using local docling.
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Args:
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text: The text content to convert.
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name: The name to use for the document (defaults to "content.md").
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format: The format of the text content ("md", "html", or "plain").
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Defaults to "md". Use "plain" for plain text without parsing.
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Returns:
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DoclingDocument representation of the text.
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Raises:
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ValueError: If the text cannot be converted or format is unsupported.
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"""
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return await TextFileHandler.text_to_docling_document(text, name, format)
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