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Merge from simplify-conv-api

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Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
This commit is contained in:
Christoph Auer 2024-10-11 15:57:08 +02:00
commit d0fccb9342
22 changed files with 286 additions and 380 deletions
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7
docling/cli/main.py
View File

@ -13,7 +13,7 @@ from docling_core.utils.file import resolve_file_source
from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend
from docling.datamodel.base_models import ConversionStatus, InputFormat
from docling.datamodel.document import ConversionResult, DocumentConversionInput
from docling.datamodel.document import ConversionResult
from docling.datamodel.pipeline_options import (
EasyOcrOptions,
PdfPipelineOptions,
@ -231,12 +231,9 @@ def convert(
}
)
# Define input files
input = DocumentConversionInput.from_paths(input_doc_paths)
start_time = time.time()
conv_results = doc_converter.convert_batch(input)
conv_results = doc_converter.convert_all(input_doc_paths)
output.mkdir(parents=True, exist_ok=True)
export_documents(

36
docling/datamodel/document.py
View File

@ -19,6 +19,7 @@ from docling_core.types.experimental import (
DocItemLabel,
DoclingDocument,
)
from docling_core.utils.file import resolve_file_source
from pydantic import BaseModel
from typing_extensions import deprecated
@ -158,8 +159,7 @@ class DocumentFormat(str, Enum):
V1 = "v1"
@deprecated("Use `ConversionResult` instead.")
class ConvertedDocument(BaseModel):
class ConversionResult(BaseModel):
input: InputDocument
status: ConversionStatus = ConversionStatus.PENDING # failure, success
@ -471,20 +471,16 @@ class ConvertedDocument(BaseModel):
yield element, cropped_im
class ConversionResult(ConvertedDocument):
pass
class _DocumentConversionInput(BaseModel):
class DocumentConversionInput(BaseModel):
_path_or_stream_iterator: Iterable[Union[Path, DocumentStream]] = None
path_or_stream_iterator: Iterable[Union[Path, str, DocumentStream]]
limits: Optional[DocumentLimits] = DocumentLimits()
def docs(
self, format_options: Dict[InputFormat, "FormatOption"]
) -> Iterable[InputDocument]:
for obj in self._path_or_stream_iterator:
for item in self.path_or_stream_iterator:
obj = resolve_file_source(item) if isinstance(item, str) else item
format = self._guess_format(obj)
if format not in format_options.keys():
_log.debug(
@ -510,6 +506,8 @@ class DocumentConversionInput(BaseModel):
limits=self.limits,
backend=backend,
)
else:
raise RuntimeError(f"Unexpected obj type in iterator: {type(obj)}")
def _guess_format(self, obj):
content = None
@ -545,21 +543,3 @@ class DocumentConversionInput(BaseModel):
return "text/html"
return None
@classmethod
def from_paths(cls, paths: Iterable[Path], limits: Optional[DocumentLimits] = None):
paths = [Path(p) for p in paths]
doc_input = cls(limits=limits)
doc_input._path_or_stream_iterator = paths
return doc_input
@classmethod
def from_streams(
cls, streams: Iterable[DocumentStream], limits: Optional[DocumentLimits] = None
):
doc_input = cls(limits=limits)
doc_input._path_or_stream_iterator = streams
return doc_input

15
docling/datamodel/pipeline_options.py
View File

@ -75,19 +75,4 @@ class PdfPipelineOptions(PipelineOptions):
Field(EasyOcrOptions(), discriminator="kind")
)
keep_page_images: Annotated[
bool,
Field(
deprecated="`keep_page_images` is depreacted, set the value of `images_scale` instead"
),
] = False # False: page images are removed in the assemble step
images_scale: Optional[float] = None # if set, the scale for generated images
@model_validator(mode="after")
def set_page_images_from_deprecated(self) -> "PdfPipelineOptions":
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
default_scale = 1.0
if self.keep_page_images and self.images_scale is None:
self.images_scale = default_scale
return self

123
docling/document_converter.py
View File

@ -1,33 +1,24 @@
import logging
import tempfile
import sys
import time
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Type
import requests
from pydantic import (
AnyHttpUrl,
BaseModel,
ConfigDict,
TypeAdapter,
ValidationError,
model_validator,
)
from typing_extensions import deprecated
from pydantic import BaseModel, ConfigDict, model_validator, validate_call
from docling.backend.abstract_backend import AbstractDocumentBackend
from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
from docling.backend.html_backend import HTMLDocumentBackend
from docling.backend.mspowerpoint_backend import MsPowerpointDocumentBackend
from docling.backend.msword_backend import MsWordDocumentBackend
from docling.datamodel.base_models import ConversionStatus, InputFormat
from docling.datamodel.base_models import ConversionStatus, DocumentStream, InputFormat
from docling.datamodel.document import (
ConversionResult,
DocumentConversionInput,
InputDocument,
_DocumentConversionInput,
)
from docling.datamodel.pipeline_options import PipelineOptions
from docling.datamodel.settings import settings
from docling.datamodel.settings import DocumentLimits, settings
from docling.pipeline.base_model_pipeline import AbstractModelPipeline
from docling.pipeline.simple_model_pipeline import SimpleModelPipeline
from docling.pipeline.standard_pdf_model_pipeline import StandardPdfModelPipeline
@ -118,16 +109,56 @@ class DocumentConverter:
Type[AbstractModelPipeline], AbstractModelPipeline
] = {}
@deprecated("Use convert_batch instead.")
def convert(self, input: DocumentConversionInput) -> Iterable[ConversionResult]:
yield from self.convert_batch(input=input)
@validate_call(config=ConfigDict(strict=True))
def convert(
self,
source: Path | str | DocumentStream, # TODO review naming
raises_on_error: bool = True,
max_num_pages: int = sys.maxsize,
max_file_size: int = sys.maxsize,
) -> ConversionResult:
def convert_batch(
self, input: DocumentConversionInput, raise_on_error: bool = False
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,
)
return next(all_res)
@validate_call(config=ConfigDict(strict=True))
def convert_all(
self,
source: Iterable[Path | str | DocumentStream], # TODO review naming
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,
) -> Iterable[ConversionResult]:
limits = DocumentLimits(
max_num_pages=max_num_pages,
max_file_size=max_file_size,
)
conv_input = _DocumentConversionInput(
path_or_stream_iterator=source,
limit=limits,
)
conv_res_iter = self._convert(conv_input)
for conv_res in conv_res_iter:
if raises_on_error and conv_res.status not in {
ConversionStatus.SUCCESS,
ConversionStatus.PARTIAL_SUCCESS,
}:
raise RuntimeError(
f"Conversion failed for: {conv_res.input.file} with status: {conv_res.status}"
)
else:
yield conv_res
def _convert(
self, conv_input: _DocumentConversionInput
) -> Iterable[ConversionResult]:
for input_batch in chunkify(
input.docs(self.format_to_options),
conv_input.docs(self.format_to_options),
settings.perf.doc_batch_size, # pass format_options
):
_log.info(f"Going to convert document batch...")
@ -142,58 +173,6 @@ class DocumentConverter:
if item is not None:
yield item
def convert_single(
self, source: Path | AnyHttpUrl | str, raise_on_error: bool = False
) -> ConversionResult:
"""Convert a single document.
Args:
source (Path | AnyHttpUrl | str): The PDF input source. Can be a path or URL.
Raises:
ValueError: If source is of unexpected type.
RuntimeError: If conversion fails.
Returns:
ConversionResult: The conversion result object.
"""
with tempfile.TemporaryDirectory() as temp_dir:
try:
http_url: AnyHttpUrl = TypeAdapter(AnyHttpUrl).validate_python(source)
res = requests.get(http_url, stream=True)
res.raise_for_status()
fname = None
# try to get filename from response header
if cont_disp := res.headers.get("Content-Disposition"):
for par in cont_disp.strip().split(";"):
# currently only handling directive "filename" (not "*filename")
if (split := par.split("=")) and split[0].strip() == "filename":
fname = "=".join(split[1:]).strip().strip("'\"") or None
break
# otherwise, use name from URL:
if fname is None:
fname = Path(http_url.path).name or self._default_download_filename
local_path = Path(temp_dir) / fname
with open(local_path, "wb") as f:
for chunk in res.iter_content(chunk_size=1024): # using 1-KB chunks
f.write(chunk)
except ValidationError:
try:
local_path = TypeAdapter(Path).validate_python(source)
except ValidationError:
raise ValueError(
f"Unexpected file path type encountered: {type(source)}"
)
conv_inp = DocumentConversionInput.from_paths(paths=[local_path])
conv_res_iter = self.convert_batch(conv_inp)
conv_res: ConversionResult = next(conv_res_iter)
if conv_res.status not in {
ConversionStatus.SUCCESS,
ConversionStatus.PARTIAL_SUCCESS,
}:
raise RuntimeError(f"Conversion failed with status: {conv_res.status}")
return conv_res
def _get_pipeline(self, doc: InputDocument) -> Optional[AbstractModelPipeline]:
fopt = self.format_to_options.get(doc.format)

23
docling/models/ds_glm_model.py
View File

@ -14,23 +14,26 @@ from docling_core.types import Ref
from docling_core.types.experimental import BoundingBox, CoordOrigin
from docling_core.types.experimental.document import DoclingDocument
from PIL import ImageDraw
from pydantic import BaseModel, ConfigDict
from docling.datamodel.base_models import Cluster
from docling.datamodel.document import ConversionResult
class GlmModel:
def __init__(self, config):
self.config = config
self.create_legacy_output = config.get("create_legacy_output", True)
class GlmOptions(BaseModel):
model_config = ConfigDict(protected_namespaces=())
create_legacy_output: bool = True
model_names: str = "" # e.g. "language;term;reference"
class GlmModel:
def __init__(self, options: GlmOptions):
self.options = options
self.create_legacy_output = self.options.create_legacy_output
self.model_names = self.config.get(
"model_names", ""
) # "language;term;reference"
load_pretrained_nlp_models()
# model = init_nlp_model(model_names="language;term;reference")
model = init_nlp_model(model_names=self.model_names)
self.model = model
self.model = init_nlp_model(model_names=self.options.model_names)
def __call__(
self, conv_res: ConversionResult

8
docling/models/layout_model.py
View File

@ -2,6 +2,7 @@ import copy
import logging
import random
import time
from pathlib import Path
from typing import Iterable, List
from docling_core.types.experimental import CoordOrigin
@ -43,11 +44,8 @@ class LayoutModel(AbstractPageModel):
FIGURE_LABEL = DocItemLabel.PICTURE
FORMULA_LABEL = DocItemLabel.FORMULA
def __init__(self, config):
self.config = config
self.layout_predictor = LayoutPredictor(
config["artifacts_path"]
) # TODO temporary
def __init__(self, artifacts_path: Path):
self.layout_predictor = LayoutPredictor(artifacts_path) # TODO temporary
def postprocess(self, clusters: List[Cluster], cells: List[Cell], page_height):
MIN_INTERSECTION = 0.2

12
docling/models/page_assemble_model.py
View File

@ -2,6 +2,8 @@ import logging
import re
from typing import Iterable, List
from pydantic import BaseModel
from docling.datamodel.base_models import (
AssembledUnit,
FigureElement,
@ -16,9 +18,13 @@ from docling.models.layout_model import LayoutModel
_log = logging.getLogger(__name__)
class PageAssembleOptions(BaseModel):
keep_images: bool = False
class PageAssembleModel(AbstractPageModel):
def __init__(self, config):
self.config = config
def __init__(self, options: PageAssembleOptions):
self.options = options
def sanitize_text(self, lines):
if len(lines) <= 1:
@ -147,7 +153,7 @@ class PageAssembleModel(AbstractPageModel):
)
# Remove page images (can be disabled)
if self.config["images_scale"] is None:
if not self.options.keep_images:
page._image_cache = {}
# Unload backend

13
docling/models/page_preprocessing_model.py
View File

@ -1,14 +1,19 @@
from typing import Iterable
from typing import Iterable, Optional
from PIL import ImageDraw
from pydantic import BaseModel
from docling.datamodel.base_models import Page
from docling.models.abstract_model import AbstractPageModel
class PagePreprocessingOptions(BaseModel):
images_scale: Optional[float]
class PagePreprocessingModel(AbstractPageModel):
def __init__(self, config):
self.config = config
def __init__(self, options: PagePreprocessingOptions):
self.options = options
def __call__(self, page_batch: Iterable[Page]) -> Iterable[Page]:
for page in page_batch:
@ -23,7 +28,7 @@ class PagePreprocessingModel(AbstractPageModel):
scale=1.0
) # puts the page image on the image cache at default scale
images_scale = self.config["images_scale"]
images_scale = self.options.images_scale
# user requested scales
if images_scale is not None:
page._default_image_scale = images_scale

16
docling/models/table_structure_model.py
View File

@ -10,19 +10,21 @@ from docling_ibm_models.tableformer.data_management.tf_predictor import TFPredic
from PIL import ImageDraw
from docling.datamodel.base_models import Page, Table, TableStructurePrediction
from docling.datamodel.pipeline_options import TableFormerMode
from docling.datamodel.pipeline_options import TableFormerMode, TableStructureOptions
from docling.models.abstract_model import AbstractPageModel
class TableStructureModel(AbstractPageModel):
def __init__(self, config):
self.config = config
self.do_cell_matching = config["do_cell_matching"]
self.mode = config["mode"]
def __init__(
self, enabled: bool, artifacts_path: Path, options: TableStructureOptions
):
self.options = options
self.do_cell_matching = self.options.do_cell_matching
self.mode = self.options.mode
self.enabled = config["enabled"]
self.enabled = enabled
if self.enabled:
artifacts_path: Path = config["artifacts_path"]
artifacts_path: Path = artifacts_path
if self.mode == TableFormerMode.ACCURATE:
artifacts_path = artifacts_path / "fat"

78
docling/pipeline/standard_pdf_model_pipeline.py
View File

@ -13,11 +13,14 @@ from docling.datamodel.pipeline_options import (
TesseractOcrOptions,
)
from docling.models.base_ocr_model import BaseOcrModel
from docling.models.ds_glm_model import GlmModel
from docling.models.ds_glm_model import GlmModel, GlmOptions
from docling.models.easyocr_model import EasyOcrModel
from docling.models.layout_model import LayoutModel
from docling.models.page_assemble_model import PageAssembleModel
from docling.models.page_preprocessing_model import PagePreprocessingModel
from docling.models.page_assemble_model import PageAssembleModel, PageAssembleOptions
from docling.models.page_preprocessing_model import (
PagePreprocessingModel,
PagePreprocessingOptions,
)
from docling.models.table_structure_model import TableStructureModel
from docling.models.tesseract_ocr_cli_model import TesseractOcrCliModel
from docling.models.tesseract_ocr_model import TesseractOcrModel
@ -32,57 +35,50 @@ class StandardPdfModelPipeline(PaginatedModelPipeline):
def __init__(self, pipeline_options: PdfPipelineOptions):
super().__init__(pipeline_options)
self.pipeline_options: PdfPipelineOptions
if not pipeline_options.artifacts_path:
artifacts_path = self.download_models_hf()
self.artifacts_path = Path(artifacts_path)
self.glm_model = GlmModel(
config={"create_legacy_output": pipeline_options.create_legacy_output}
options=GlmOptions(
create_legacy_output=pipeline_options.create_legacy_output
)
)
ocr_model: BaseOcrModel
if isinstance(pipeline_options.ocr_options, EasyOcrOptions):
ocr_model = EasyOcrModel(
enabled=pipeline_options.do_ocr,
options=pipeline_options.ocr_options,
)
elif isinstance(pipeline_options.ocr_options, TesseractCliOcrOptions):
ocr_model = TesseractOcrCliModel(
enabled=pipeline_options.do_ocr,
options=pipeline_options.ocr_options,
)
elif isinstance(pipeline_options.ocr_options, TesseractOcrOptions):
ocr_model = TesseractOcrModel(
enabled=pipeline_options.do_ocr,
options=pipeline_options.ocr_options,
)
else:
if (ocr_model := self.get_ocr_model()) is None:
raise RuntimeError(
f"The specified OCR kind is not supported: {pipeline_options.ocr_options.kind}."
)
self.model_pipe = [
# Pre-processing
PagePreprocessingModel(
config={"images_scale": pipeline_options.images_scale}
options=PagePreprocessingOptions(
images_scale=pipeline_options.images_scale
)
),
# OCR
ocr_model,
# Layout model
LayoutModel(
config={
"artifacts_path": artifacts_path
artifacts_path=artifacts_path
/ StandardPdfModelPipeline._layout_model_path
}
),
# Table structure model
TableStructureModel(
config={
"artifacts_path": artifacts_path
enabled=pipeline_options.do_table_structure,
artifacts_path=artifacts_path
/ StandardPdfModelPipeline._table_model_path,
"enabled": pipeline_options.do_table_structure,
"do_cell_matching": pipeline_options.table_structure_options.do_cell_matching,
"mode": pipeline_options.table_structure_options.mode,
}
options=pipeline_options.table_structure_options,
),
# Page assemble
PageAssembleModel(
options=PageAssembleOptions(
keep_images=pipeline_options.images_scale is not None
)
),
PageAssembleModel(config={"images_scale": pipeline_options.images_scale}),
]
self.enrichment_pipe = [
@ -104,6 +100,24 @@ class StandardPdfModelPipeline(PaginatedModelPipeline):
return Path(download_path)
def get_ocr_model(self) -> Optional[BaseOcrModel]:
if isinstance(self.pipeline_options.ocr_options, EasyOcrOptions):
return EasyOcrModel(
enabled=self.pipeline_options.do_ocr,
options=self.pipeline_options.ocr_options,
)
elif isinstance(self.pipeline_options.ocr_options, TesseractCliOcrOptions):
return TesseractOcrCliModel(
enabled=self.pipeline_options.do_ocr,
options=self.pipeline_options.ocr_options,
)
elif isinstance(self.pipeline_options.ocr_options, TesseractOcrOptions):
return TesseractOcrModel(
enabled=self.pipeline_options.do_ocr,
options=self.pipeline_options.ocr_options,
)
return None
def initialize_page(self, doc: InputDocument, page: Page) -> Page:
page._backend = doc._backend.load_page(page.page_no)
page.size = page._backend.get_size()

11
examples/batch_convert.py
View File

@ -7,7 +7,7 @@ from typing import Iterable
import yaml
from docling.datamodel.base_models import ConversionStatus
from docling.datamodel.document import ConversionResult, DocumentConversionInput
from docling.datamodel.document import ConversionResult
from docling.document_converter import DocumentConverter
_log = logging.getLogger(__name__)
@ -125,18 +125,19 @@ def main():
doc_converter = DocumentConverter()
input = DocumentConversionInput.from_paths(input_doc_paths)
start_time = time.time()
conv_results = doc_converter.convert_batch(input)
conv_results = doc_converter.convert_all(
input_doc_paths,
raises_on_error=False, # to let conversion run through all and examine results at the end
)
success_count, partial_success_count, failure_count = export_documents(
conv_results, output_dir=Path("./scratch")
)
end_time = time.time() - start_time
_log.info(f"All documents were converted in {end_time:.2f} seconds.")
_log.info(f"Document conversion complete in {end_time:.2f} seconds.")
if failure_count > 0:
raise RuntimeError(

30
examples/custom_convert.py
View File

@ -5,9 +5,14 @@ from pathlib import Path
from typing import Iterable
from docling.datamodel.base_models import ConversionStatus, InputFormat
from docling.datamodel.document import ConversionResult, DocumentConversionInput
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.datamodel.document import ConversionResult
from docling.datamodel.pipeline_options import (
PdfPipelineOptions,
TesseractCliOcrOptions,
TesseractOcrOptions,
)
from docling.document_converter import DocumentConverter, FormatOption, PdfFormatOption
from docling.pipeline.standard_pdf_model_pipeline import StandardPdfModelPipeline
_log = logging.getLogger(__name__)
@ -60,9 +65,7 @@ def export_documents(
def main():
logging.basicConfig(level=logging.INFO)
input_doc_paths = [
Path("./tests/data/2206.01062.pdf"),
]
input_doc_path = Path("./tests/data/2206.01062.pdf")
###########################################################################
@ -147,24 +150,13 @@ def main():
###########################################################################
# Define input files
input = DocumentConversionInput.from_paths(input_doc_paths)
start_time = time.time()
conv_results = doc_converter.convert_batch(input)
success_count, failure_count = export_documents(
conv_results, output_dir=Path("./scratch")
)
conv_result = doc_converter.convert(input_doc_path)
end_time = time.time() - start_time
_log.info(f"All documents were converted in {end_time:.2f} seconds.")
if failure_count > 0:
raise RuntimeError(
f"The example failed converting {failure_count} on {len(input_doc_paths)}."
)
_log.info(f"Document converted in {end_time:.2f} seconds.")
if __name__ == "__main__":

37
examples/export_figures.py
View File

@ -2,13 +2,7 @@ import logging
import time
from pathlib import Path
from docling.datamodel.base_models import (
ConversionStatus,
FigureElement,
InputFormat,
Table,
)
from docling.datamodel.document import DocumentConversionInput
from docling.datamodel.base_models import FigureElement, InputFormat, Table
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.document_converter import DocumentConverter, PdfFormatOption
@ -20,13 +14,9 @@ IMAGE_RESOLUTION_SCALE = 2.0
def main():
logging.basicConfig(level=logging.INFO)
input_doc_paths = [
Path("./tests/data/2206.01062.pdf"),
]
input_doc_path = Path("./tests/data/2206.01062.pdf")
output_dir = Path("./scratch")
input_files = DocumentConversionInput.from_paths(input_doc_paths)
# Important: For operating with page images, we must keep them, otherwise the DocumentConverter
# will destroy them for cleaning up memory.
# This is done by setting AssembleOptions.images_scale, which also defines the scale of images.
@ -42,17 +32,9 @@ def main():
start_time = time.time()
conv_results = doc_converter.convert_batch(input_files)
conv_res = doc_converter.convert(input_doc_path)
success_count = 0
failure_count = 0
output_dir.mkdir(parents=True, exist_ok=True)
for conv_res in conv_results:
if conv_res.status != ConversionStatus.SUCCESS:
_log.info(f"Document {conv_res.input.file} failed to convert.")
failure_count += 1
continue
doc_filename = conv_res.input.file.stem
# Export page images
@ -66,22 +48,13 @@ def main():
for element, image in conv_res.render_element_images(
element_types=(FigureElement, Table)
):
element_image_filename = (
output_dir / f"{doc_filename}-element-{element.id}.png"
)
element_image_filename = output_dir / f"{doc_filename}-element-{element.id}.png"
with element_image_filename.open("wb") as fp:
image.save(fp, "PNG")
success_count += 1
end_time = time.time() - start_time
_log.info(f"All documents were converted in {end_time:.2f} seconds.")
if failure_count > 0:
raise RuntimeError(
f"The example failed converting {failure_count} on {len(input_doc_paths)}."
)
_log.info(f"Document converted and figures exported in {end_time:.2f} seconds.")
if __name__ == "__main__":

32
examples/export_multimodal.py
View File

@ -5,8 +5,7 @@ from pathlib import Path
import pandas as pd
from docling.datamodel.base_models import ConversionStatus, InputFormat
from docling.datamodel.document import DocumentConversionInput
from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.utils.export import generate_multimodal_pages
@ -19,13 +18,9 @@ IMAGE_RESOLUTION_SCALE = 2.0
def main():
logging.basicConfig(level=logging.INFO)
input_doc_paths = [
Path("./tests/data/2206.01062.pdf"),
]
input_doc_path = Path("./tests/data/2206.01062.pdf")
output_dir = Path("./scratch")
input_files = DocumentConversionInput.from_paths(input_doc_paths)
# Important: For operating with page images, we must keep them, otherwise the DocumentConverter
# will destroy them for cleaning up memory.
# This is done by setting AssembleOptions.images_scale, which also defines the scale of images.
@ -41,16 +36,9 @@ def main():
start_time = time.time()
converted_docs = doc_converter.convert_batch(input_files)
conv_res = doc_converter.convert(input_doc_path)
success_count = 0
failure_count = 0
output_dir.mkdir(parents=True, exist_ok=True)
for doc in converted_docs:
if doc.status != ConversionStatus.SUCCESS:
_log.info(f"Document {doc.input.file} failed to convert.")
failure_count += 1
continue
rows = []
for (
@ -60,14 +48,14 @@ def main():
page_cells,
page_segments,
page,
) in generate_multimodal_pages(doc):
) in generate_multimodal_pages(conv_res):
dpi = page._default_image_scale * 72
rows.append(
{
"document": doc.input.file.name,
"hash": doc.input.document_hash,
"document": conv_res.input.file.name,
"hash": conv_res.input.document_hash,
"page_hash": page.page_hash,
"image": {
"width": page.image.width,
@ -87,7 +75,6 @@ def main():
},
}
)
success_count += 1
# Generate one parquet from all documents
df = pd.json_normalize(rows)
@ -97,11 +84,8 @@ def main():
end_time = time.time() - start_time
_log.info(f"All documents were converted in {end_time:.2f} seconds.")
if failure_count > 0:
raise RuntimeError(
f"The example failed converting {failure_count} on {len(input_doc_paths)}."
_log.info(
f"Document converted and multimodal pages generated in {end_time:.2f} seconds."
)
# This block demonstrates how the file can be opened with the HF datasets library

30
examples/export_tables.py
View File

@ -4,8 +4,6 @@ from pathlib import Path
import pandas as pd
from docling.datamodel.base_models import ConversionStatus
from docling.datamodel.document import DocumentConversionInput
from docling.document_converter import DocumentConverter
_log = logging.getLogger(__name__)
@ -14,27 +12,16 @@ _log = logging.getLogger(__name__)
def main():
logging.basicConfig(level=logging.INFO)
input_doc_paths = [
Path("./tests/data/2206.01062.pdf"),
]
input_doc_path = Path("./tests/data/2206.01062.pdf")
output_dir = Path("./scratch")
input_files = DocumentConversionInput.from_paths(input_doc_paths)
doc_converter = DocumentConverter()
start_time = time.time()
conv_results = doc_converter.convert_batch(input_files)
conv_res = doc_converter.convert(input_doc_path)
success_count = 0
failure_count = 0
output_dir.mkdir(parents=True, exist_ok=True)
for conv_res in conv_results:
if conv_res.status != ConversionStatus.SUCCESS:
_log.info(f"Document {conv_res.input.file} failed to convert.")
failure_count += 1
continue
doc_filename = conv_res.input.file.stem
@ -50,23 +37,14 @@ def main():
table_df.to_csv(element_csv_filename)
# Save the table as html
element_html_filename = (
output_dir / f"{doc_filename}-table-{table_ix+1}.html"
)
element_html_filename = output_dir / f"{doc_filename}-table-{table_ix+1}.html"
_log.info(f"Saving HTML table to {element_html_filename}")
with element_html_filename.open("w") as fp:
fp.write(table.export_to_html())
success_count += 1
end_time = time.time() - start_time
_log.info(f"All documents were converted in {end_time:.2f} seconds.")
if failure_count > 0:
raise RuntimeError(
f"The example failed converting {failure_count} on {len(input_doc_paths)}."
)
_log.info(f"Document converted and tables exported in {end_time:.2f} seconds.")
if __name__ == "__main__":

2
examples/minimal.py
View File

@ -2,7 +2,7 @@ from docling.document_converter import DocumentConverter
source = "https://arxiv.org/pdf/2408.09869" # PDF path or URL
converter = DocumentConverter()
result = converter.convert_single(source)
result = converter.convert(source)
print(result.output.export_to_markdown()) # output: ## Docling Technical Report [...]"
# if the legacy output is needed, use this version
# print(result.render_as_markdown_v1()) # output: ## Docling Technical Report [...]"

30
examples/run_with_formats.py
View File

@ -4,7 +4,6 @@ from pathlib import Path
from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend
from docling.datamodel.base_models import InputFormat
from docling.datamodel.document import DocumentConversionInput
from docling.document_converter import (
DocumentConverter,
PdfFormatOption,
@ -25,7 +24,6 @@ input_paths = [
Path("tests/data/2206.01062.pdf"),
# Path("tests/data/2305.03393v1-pg9-img.png"),
]
input = DocumentConversionInput.from_paths(input_paths)
## for defaults use:
# doc_converter = DocumentConverter()
@ -50,12 +48,36 @@ doc_converter = DocumentConverter( # all of the below is optional, has internal
},
)
conv_results = doc_converter.convert_batch(input)
doc_converter = DocumentConverter( # all of the below is optional, has internal defaults.
pdf=None,
docx=WordFormatOption(
pipeline_cls=SimpleModelPipeline # , backend=MsWordDocumentBackend
),
formats=[
InputFormat.PDF,
# InputFormat.IMAGE,
InputFormat.DOCX,
InputFormat.HTML,
InputFormat.PPTX,
], # whitelist formats, other files are ignored.
format_options={
InputFormat.PDF: PdfFormatOption(
pipeline_cls=StandardPdfModelPipeline, backend=PyPdfiumDocumentBackend
), # PdfFormatOption(backend=PyPdfiumDocumentBackend),
InputFormat.DOCX: WordFormatOption(
pipeline_cls=SimpleModelPipeline # , backend=MsWordDocumentBackend
),
# InputFormat.IMAGE: PdfFormatOption(),
},
)
conv_results = doc_converter.convert_all(input_paths)
for res in conv_results:
out_path = Path("./scratch")
print(
f"Document {res.input.file.name} converted with status {res.status}."
f"Document {res.input.file.name} converted."
f"\nSaved markdown output to: {str(out_path)}"
)
# print(res.experimental.export_to_markdown())

2
tests/test_e2e_conversion.py
View File

@ -48,7 +48,7 @@ def test_e2e_conversions():
for pdf_path in pdf_paths:
print(f"converting {pdf_path}")
doc_result: ConversionResult = converter.convert_single(pdf_path)
doc_result: ConversionResult = converter.convert(pdf_path)
verify_conversion_result_v1(
input_path=pdf_path, doc_result=doc_result, generate=GENERATE_V1

2
tests/test_e2e_ocr_conversion.py
View File

@ -89,7 +89,7 @@ def test_e2e_conversions():
for pdf_path in pdf_paths:
print(f"converting {pdf_path}")
doc_result: ConversionResult = converter.convert_single(pdf_path)
doc_result: ConversionResult = converter.convert(pdf_path)
# Save conversions
# save_output(pdf_path, doc_result, None)

26
tests/test_interfaces.py
View File

@ -5,7 +5,6 @@ import pytest
from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
from docling.datamodel.base_models import DocumentStream, InputFormat
from docling.datamodel.document import ConversionResult, DocumentConversionInput
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.document_converter import DocumentConverter, PdfFormatOption
@ -37,39 +36,24 @@ def converter():
return converter
def test_convert_single(converter: DocumentConverter):
def test_convert_path(converter: DocumentConverter):
pdf_path = get_pdf_path()
print(f"converting {pdf_path}")
doc_result: ConversionResult = converter.convert_single(pdf_path)
doc_result = converter.convert(pdf_path)
verify_conversion_result_v1(input_path=pdf_path, doc_result=doc_result)
verify_conversion_result_v2(input_path=pdf_path, doc_result=doc_result)
def test_batch_path(converter: DocumentConverter):
pdf_path = get_pdf_path()
print(f"converting {pdf_path}")
conv_input = DocumentConversionInput.from_paths([pdf_path])
results = converter.convert_batch(conv_input)
for doc_result in results:
verify_conversion_result_v1(input_path=pdf_path, doc_result=doc_result)
verify_conversion_result_v2(input_path=pdf_path, doc_result=doc_result)
def test_batch_bytes(converter: DocumentConverter):
def test_convert_stream(converter: DocumentConverter):
pdf_path = get_pdf_path()
print(f"converting {pdf_path}")
buf = BytesIO(pdf_path.open("rb").read())
docs = [DocumentStream(name=pdf_path.name, stream=buf)]
conv_input = DocumentConversionInput.from_streams(docs)
stream = DocumentStream(name=pdf_path.name, stream=buf)
results = converter.convert_batch(conv_input)
for doc_result in results:
doc_result = converter.convert(stream)
verify_conversion_result_v1(input_path=pdf_path, doc_result=doc_result)
verify_conversion_result_v2(input_path=pdf_path, doc_result=doc_result)

2
tests/test_options.py
View File

@ -39,6 +39,6 @@ def test_e2e_conversions(test_doc_path):
for converter in get_converters_with_table_options():
print(f"converting {test_doc_path}")
doc_result: ConversionResult = converter.convert_single(test_doc_path)
doc_result: ConversionResult = converter.convert(test_doc_path)
assert doc_result.status == ConversionStatus.SUCCESS

3
tests/verify_utils.py
View File

@ -1,4 +1,5 @@
import json
import warnings
from pathlib import Path
from typing import List
@ -234,6 +235,8 @@ def verify_conversion_result_v1(
doc_pred_pages: List[Page] = doc_result.pages
doc_pred: DsDocument = doc_result.legacy_output
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
doc_pred_md = doc_result.render_as_markdown()
doc_pred_dt = doc_result.render_as_doctags()