Docling/docling/document_converter.py

import hashlib
import logging
import sys
import time
from collections.abc import Iterable, Iterator
from functools import partial
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Type, Union

from pydantic import BaseModel, ConfigDict, model_validator, validate_call

from docling.backend.abstract_backend import AbstractDocumentBackend
from docling.backend.asciidoc_backend import AsciiDocBackend
from docling.backend.csv_backend import CsvDocumentBackend
from docling.backend.docling_parse_v4_backend import DoclingParseV4DocumentBackend
from docling.backend.html_backend import HTMLDocumentBackend
from docling.backend.json.docling_json_backend import DoclingJSONBackend
from docling.backend.md_backend import MarkdownDocumentBackend
from docling.backend.msexcel_backend import MsExcelDocumentBackend
from docling.backend.mspowerpoint_backend import MsPowerpointDocumentBackend
from docling.backend.msword_backend import MsWordDocumentBackend
from docling.backend.noop_backend import NoOpBackend
from docling.backend.xml.jats_backend import JatsDocumentBackend
from docling.backend.xml.uspto_backend import PatentUsptoDocumentBackend
from docling.datamodel.base_models import (
    ConversionStatus,
    DoclingComponentType,
    DocumentStream,
    ErrorItem,
    InputFormat,
)
from docling.datamodel.document import (
    ConversionResult,
    InputDocument,
    _DocumentConversionInput,
)
from docling.datamodel.pipeline_options import PipelineOptions
from docling.datamodel.settings import (
    DEFAULT_PAGE_RANGE,
    DocumentLimits,
    PageRange,
    settings,
)
from docling.exceptions import ConversionError
from docling.pipeline.asr_pipeline import AsrPipeline
from docling.pipeline.base_pipeline import BasePipeline
from docling.pipeline.simple_pipeline import SimplePipeline
from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline
from docling.utils.utils import chunkify

_log = logging.getLogger(__name__)


class FormatOption(BaseModel):
    pipeline_cls: Type[BasePipeline]
    pipeline_options: Optional[PipelineOptions] = None
    backend: Type[AbstractDocumentBackend]

    model_config = ConfigDict(arbitrary_types_allowed=True)

    @model_validator(mode="after")
    def set_optional_field_default(self) -> "FormatOption":
        if self.pipeline_options is None:
            self.pipeline_options = self.pipeline_cls.get_default_options()
        return self


class CsvFormatOption(FormatOption):
    pipeline_cls: Type = SimplePipeline
    backend: Type[AbstractDocumentBackend] = CsvDocumentBackend


class ExcelFormatOption(FormatOption):
    pipeline_cls: Type = SimplePipeline
    backend: Type[AbstractDocumentBackend] = MsExcelDocumentBackend


class WordFormatOption(FormatOption):
    pipeline_cls: Type = SimplePipeline
    backend: Type[AbstractDocumentBackend] = MsWordDocumentBackend


class PowerpointFormatOption(FormatOption):
    pipeline_cls: Type = SimplePipeline
    backend: Type[AbstractDocumentBackend] = MsPowerpointDocumentBackend


class MarkdownFormatOption(FormatOption):
    pipeline_cls: Type = SimplePipeline
    backend: Type[AbstractDocumentBackend] = MarkdownDocumentBackend


class AsciiDocFormatOption(FormatOption):
    pipeline_cls: Type = SimplePipeline
    backend: Type[AbstractDocumentBackend] = AsciiDocBackend


class HTMLFormatOption(FormatOption):
    pipeline_cls: Type = SimplePipeline
    backend: Type[AbstractDocumentBackend] = HTMLDocumentBackend


class PatentUsptoFormatOption(FormatOption):
    pipeline_cls: Type = SimplePipeline
    backend: Type[PatentUsptoDocumentBackend] = PatentUsptoDocumentBackend


class XMLJatsFormatOption(FormatOption):
    pipeline_cls: Type = SimplePipeline
    backend: Type[AbstractDocumentBackend] = JatsDocumentBackend


class ImageFormatOption(FormatOption):
    pipeline_cls: Type = StandardPdfPipeline
    backend: Type[AbstractDocumentBackend] = DoclingParseV4DocumentBackend


class PdfFormatOption(FormatOption):
    pipeline_cls: Type = StandardPdfPipeline
    backend: Type[AbstractDocumentBackend] = DoclingParseV4DocumentBackend


class AudioFormatOption(FormatOption):
    pipeline_cls: Type = AsrPipeline
    backend: Type[AbstractDocumentBackend] = NoOpBackend


def _get_default_option(format: InputFormat) -> FormatOption:
    format_to_default_options = {
        InputFormat.CSV: FormatOption(
            pipeline_cls=SimplePipeline, backend=CsvDocumentBackend
        ),
        InputFormat.XLSX: FormatOption(
            pipeline_cls=SimplePipeline, backend=MsExcelDocumentBackend
        ),
        InputFormat.DOCX: FormatOption(
            pipeline_cls=SimplePipeline, backend=MsWordDocumentBackend
        ),
        InputFormat.PPTX: FormatOption(
            pipeline_cls=SimplePipeline, backend=MsPowerpointDocumentBackend
        ),
        InputFormat.MD: FormatOption(
            pipeline_cls=SimplePipeline, backend=MarkdownDocumentBackend
        ),
        InputFormat.ASCIIDOC: FormatOption(
            pipeline_cls=SimplePipeline, backend=AsciiDocBackend
        ),
        InputFormat.HTML: FormatOption(
            pipeline_cls=SimplePipeline, backend=HTMLDocumentBackend
        ),
        InputFormat.XML_USPTO: FormatOption(
            pipeline_cls=SimplePipeline, backend=PatentUsptoDocumentBackend
        ),
        InputFormat.XML_JATS: FormatOption(
            pipeline_cls=SimplePipeline, backend=JatsDocumentBackend
        ),
        InputFormat.IMAGE: FormatOption(
            pipeline_cls=StandardPdfPipeline, backend=DoclingParseV4DocumentBackend
        ),
        InputFormat.PDF: FormatOption(
            pipeline_cls=StandardPdfPipeline, backend=DoclingParseV4DocumentBackend
        ),
        InputFormat.JSON_DOCLING: FormatOption(
            pipeline_cls=SimplePipeline, backend=DoclingJSONBackend
        ),
        InputFormat.AUDIO: FormatOption(pipeline_cls=AsrPipeline, backend=NoOpBackend),
    }
    if (options := format_to_default_options.get(format)) is not None:
        return options
    else:
        raise RuntimeError(f"No default options configured for {format}")


class DocumentConverter:
    _default_download_filename = "file"

    def __init__(
        self,
        allowed_formats: Optional[List[InputFormat]] = None,
        format_options: Optional[Dict[InputFormat, FormatOption]] = None,
    ):
        self.allowed_formats = (
            allowed_formats if allowed_formats is not None else list(InputFormat)
        )
        self.format_to_options = {
            format: (
                _get_default_option(format=format)
                if (custom_option := (format_options or {}).get(format)) is None
                else custom_option
            )
            for format in self.allowed_formats
        }
        self.initialized_pipelines: Dict[
            Tuple[Type[BasePipeline], str], BasePipeline
        ] = {}

    def _get_initialized_pipelines(
        self,
    ) -> dict[tuple[Type[BasePipeline], str], BasePipeline]:
        return self.initialized_pipelines

    def _get_pipeline_options_hash(self, pipeline_options: PipelineOptions) -> str:
        """Generate a hash of pipeline options to use as part of the cache key."""
        options_str = str(pipeline_options.model_dump())
        return hashlib.md5(
            options_str.encode("utf-8"), usedforsecurity=False
        ).hexdigest()

    def initialize_pipeline(self, format: InputFormat):
        """Initialize the conversion pipeline for the selected format."""
        pipeline = self._get_pipeline(doc_format=format)
        if pipeline is None:
            raise ConversionError(
                f"No pipeline could be initialized for format {format}"
            )

    @validate_call(config=ConfigDict(strict=True))
    def convert(
        self,
        source: Union[Path, str, DocumentStream],  # TODO review naming
        headers: Optional[Dict[str, str]] = None,
        raises_on_error: bool = True,
        max_num_pages: int = sys.maxsize,
        max_file_size: int = sys.maxsize,
        page_range: PageRange = DEFAULT_PAGE_RANGE,
    ) -> ConversionResult:
        all_res = self.convert_all(
            source=[source],
            raises_on_error=raises_on_error,
            max_num_pages=max_num_pages,
            max_file_size=max_file_size,
            headers=headers,
            page_range=page_range,
        )
        return next(all_res)

    @validate_call(config=ConfigDict(strict=True))
    def convert_all(
        self,
        source: Iterable[Union[Path, str, DocumentStream]],  # TODO review naming
        headers: Optional[Dict[str, str]] = None,
        raises_on_error: bool = True,  # True: raises on first conversion error; False: does not raise on conv error
        max_num_pages: int = sys.maxsize,
        max_file_size: int = sys.maxsize,
        page_range: PageRange = DEFAULT_PAGE_RANGE,
    ) -> Iterator[ConversionResult]:
        limits = DocumentLimits(
            max_num_pages=max_num_pages,
            max_file_size=max_file_size,
            page_range=page_range,
        )
        conv_input = _DocumentConversionInput(
            path_or_stream_iterator=source, limits=limits, headers=headers
        )
        conv_res_iter = self._convert(conv_input, raises_on_error=raises_on_error)

        had_result = False
        for conv_res in conv_res_iter:
            had_result = True
            if raises_on_error and conv_res.status not in {
                ConversionStatus.SUCCESS,
                ConversionStatus.PARTIAL_SUCCESS,
            }:
                raise ConversionError(
                    f"Conversion failed for: {conv_res.input.file} with status: {conv_res.status}"
                )
            else:
                yield conv_res

        if not had_result and raises_on_error:
            raise ConversionError(
                "Conversion failed because the provided file has no recognizable format or it wasn't in the list of allowed formats."
            )

    def _convert(
        self, conv_input: _DocumentConversionInput, raises_on_error: bool
    ) -> Iterator[ConversionResult]:
        start_time = time.monotonic()

        for input_batch in chunkify(
            conv_input.docs(self.format_to_options),
            settings.perf.doc_batch_size,  # pass format_options
        ):
            _log.info("Going to convert document batch...")

            # parallel processing only within input_batch
            # with ThreadPoolExecutor(
            #    max_workers=settings.perf.doc_batch_concurrency
            # ) as pool:
            #   yield from pool.map(self.process_document, input_batch)
            # Note: PDF backends are not thread-safe, thread pool usage was disabled.

            for item in map(
                partial(self._process_document, raises_on_error=raises_on_error),
                input_batch,
            ):
                elapsed = time.monotonic() - start_time
                start_time = time.monotonic()
                _log.info(
                    f"Finished converting document {item.input.file.name} in {elapsed:.2f} sec."
                )
                yield item

    def _get_pipeline(self, doc_format: InputFormat) -> Optional[BasePipeline]:
        """Retrieve or initialize a pipeline, reusing instances based on class and options."""
        fopt = self.format_to_options.get(doc_format)

        if fopt is None or fopt.pipeline_options is None:
            return None

        pipeline_class = fopt.pipeline_cls
        pipeline_options = fopt.pipeline_options
        options_hash = self._get_pipeline_options_hash(pipeline_options)

        # Use a composite key to cache pipelines
        cache_key = (pipeline_class, options_hash)

        if cache_key not in self.initialized_pipelines:
            _log.info(
                f"Initializing pipeline for {pipeline_class.__name__} with options hash {options_hash}"
            )
            self.initialized_pipelines[cache_key] = pipeline_class(
                pipeline_options=pipeline_options
            )
        else:
            _log.debug(
                f"Reusing cached pipeline for {pipeline_class.__name__} with options hash {options_hash}"
            )

        return self.initialized_pipelines[cache_key]

    def _process_document(
        self, in_doc: InputDocument, raises_on_error: bool
    ) -> ConversionResult:
        valid = (
            self.allowed_formats is not None and in_doc.format in self.allowed_formats
        )
        if valid:
            conv_res = self._execute_pipeline(in_doc, raises_on_error=raises_on_error)
        else:
            error_message = f"File format not allowed: {in_doc.file}"
            if raises_on_error:
                raise ConversionError(error_message)
            else:
                error_item = ErrorItem(
                    component_type=DoclingComponentType.USER_INPUT,
                    module_name="",
                    error_message=error_message,
                )
                conv_res = ConversionResult(
                    input=in_doc, status=ConversionStatus.SKIPPED, errors=[error_item]
                )

        return conv_res

    def _execute_pipeline(
        self, in_doc: InputDocument, raises_on_error: bool
    ) -> ConversionResult:
        if in_doc.valid:
            pipeline = self._get_pipeline(in_doc.format)
            if pipeline is not None:
                conv_res = pipeline.execute(in_doc, raises_on_error=raises_on_error)
            else:
                if raises_on_error:
                    raise ConversionError(
                        f"No pipeline could be initialized for {in_doc.file}."
                    )
                else:
                    conv_res = ConversionResult(
                        input=in_doc,
                        status=ConversionStatus.FAILURE,
                    )
        else:
            if raises_on_error:
                raise ConversionError(f"Input document {in_doc.file} is not valid.")
            else:
                # invalid doc or not of desired format
                conv_res = ConversionResult(
                    input=in_doc,
                    status=ConversionStatus.FAILURE,
                )
                # TODO add error log why it failed.

        return conv_res