mirror of
https://github.com/DS4SD/docling.git
synced 2025-07-31 14:34:40 +00:00
Update lock
Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
This commit is contained in:
Christoph Auer
commit
e49fa7ec4f
2
.github/workflows/checks.yml
vendored
2
.github/workflows/checks.yml
vendored
@ -28,7 +28,7 @@ jobs:
|
||||
run: |
|
||||
for file in docs/examples/*.py; do
|
||||
# Skip batch_convert.py
|
||||
if [[ "$(basename "$file")" =~ ^(batch_convert|minimal|export_multimodal|custom_convert|develop_picture_enrichment|rapidocr_with_custom_models|offline_convert).py ]]; then
|
||||
if [[ "$(basename "$file")" =~ ^(batch_convert|minimal|export_multimodal|custom_convert|develop_picture_enrichment|rapidocr_with_custom_models|offline_convert|pictures_description|pictures_description_api).py ]]; then
|
||||
echo "Skipping $file"
|
||||
continue
|
||||
fi
|
||||
|
||||
27
CHANGELOG.md
27
CHANGELOG.md
@ -1,3 +1,30 @@
|
||||
## [v2.20.0](https://github.com/DS4SD/docling/releases/tag/v2.20.0) - 2025-02-07
|
||||
|
||||
### Feature
|
||||
|
||||
* Describe pictures using vision models ([#259](https://github.com/DS4SD/docling/issues/259)) ([`4cc6e3e`](https://github.com/DS4SD/docling/commit/4cc6e3ea5e858b367136acc729b723ea0552d22a))
|
||||
|
||||
### Fix
|
||||
|
||||
* Remove unused httpx ([#919](https://github.com/DS4SD/docling/issues/919)) ([`c18f47c`](https://github.com/DS4SD/docling/commit/c18f47c5c032c49bf3175aecd2236df37c0e9ae1))
|
||||
|
||||
## [v2.19.0](https://github.com/DS4SD/docling/releases/tag/v2.19.0) - 2025-02-07
|
||||
|
||||
### Feature
|
||||
|
||||
* New artifacts path and CLI utility ([#876](https://github.com/DS4SD/docling/issues/876)) ([`ed74fe2`](https://github.com/DS4SD/docling/commit/ed74fe2ec0a702834f0deacfdb5717c8c587dab1))
|
||||
|
||||
### Fix
|
||||
|
||||
* **markdown:** Handle nested lists ([#910](https://github.com/DS4SD/docling/issues/910)) ([`90b766e`](https://github.com/DS4SD/docling/commit/90b766e2ae1695a759191df37c272efc09be5ee3))
|
||||
* Test cases for RTL programmatic PDFs and fixes for the formula model ([#903](https://github.com/DS4SD/docling/issues/903)) ([`9114ada`](https://github.com/DS4SD/docling/commit/9114ada7bc4dd45ce0046de2f9d00a80ccb25c79))
|
||||
* **msword_backend:** Handle conversion error in label parsing ([#896](https://github.com/DS4SD/docling/issues/896)) ([`722a6eb`](https://github.com/DS4SD/docling/commit/722a6eb7b994a0261312a356df80b2fced121812))
|
||||
* Enrichment models batch size and expose picture classifier ([#878](https://github.com/DS4SD/docling/issues/878)) ([`5ad6de0`](https://github.com/DS4SD/docling/commit/5ad6de05600315617b574bd12af553e00b4d316e))
|
||||
|
||||
### Documentation
|
||||
|
||||
* Introduce example with custom models for RapidOCR ([#874](https://github.com/DS4SD/docling/issues/874)) ([`6d3fea0`](https://github.com/DS4SD/docling/commit/6d3fea019635bd6ca94bd36c3928b28c245d638d))
|
||||
|
||||
## [v2.18.0](https://github.com/DS4SD/docling/releases/tag/v2.18.0) - 2025-02-03
|
||||
|
||||
### Feature
|
||||
|
||||
@ -36,7 +36,7 @@ _STOP_MARKER = f"#_#_{_MARKER_BODY}_STOP_#_#"
|
||||
|
||||
|
||||
class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
def shorten_underscore_sequences(self, markdown_text: str, max_length: int = 10):
|
||||
def _shorten_underscore_sequences(self, markdown_text: str, max_length: int = 10):
|
||||
# This regex will match any sequence of underscores
|
||||
pattern = r"_+"
|
||||
|
||||
@ -81,7 +81,7 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
# very long sequences of underscores will lead to unnecessary long processing times.
|
||||
# In any proper Markdown files, underscores have to be escaped,
|
||||
# otherwise they represent emphasis (bold or italic)
|
||||
self.markdown = self.shorten_underscore_sequences(text_stream)
|
||||
self.markdown = self._shorten_underscore_sequences(text_stream)
|
||||
if isinstance(self.path_or_stream, Path):
|
||||
with open(self.path_or_stream, "r", encoding="utf-8") as f:
|
||||
md_content = f.read()
|
||||
@ -89,7 +89,7 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
# very long sequences of underscores will lead to unnecessary long processing times.
|
||||
# In any proper Markdown files, underscores have to be escaped,
|
||||
# otherwise they represent emphasis (bold or italic)
|
||||
self.markdown = self.shorten_underscore_sequences(md_content)
|
||||
self.markdown = self._shorten_underscore_sequences(md_content)
|
||||
self.valid = True
|
||||
|
||||
_log.debug(self.markdown)
|
||||
@ -99,7 +99,7 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
) from e
|
||||
return
|
||||
|
||||
def close_table(self, doc: DoclingDocument):
|
||||
def _close_table(self, doc: DoclingDocument):
|
||||
if self.in_table:
|
||||
_log.debug("=== TABLE START ===")
|
||||
for md_table_row in self.md_table_buffer:
|
||||
@ -156,30 +156,35 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
doc.add_table(data=table_data)
|
||||
return
|
||||
|
||||
def process_inline_text(
|
||||
self, parent_element: Optional[NodeItem], doc: DoclingDocument
|
||||
def _process_inline_text(
|
||||
self, parent_item: Optional[NodeItem], doc: DoclingDocument
|
||||
):
|
||||
txt = " ".join(self.inline_texts)
|
||||
if len(txt) > 0:
|
||||
doc.add_text(
|
||||
label=DocItemLabel.PARAGRAPH,
|
||||
parent=parent_element,
|
||||
parent=parent_item,
|
||||
text=txt,
|
||||
)
|
||||
self.inline_texts = []
|
||||
|
||||
def iterate_elements(
|
||||
def _iterate_elements(
|
||||
self,
|
||||
element: marko.element.Element,
|
||||
depth: int,
|
||||
doc: DoclingDocument,
|
||||
parent_element: Optional[NodeItem] = None,
|
||||
visited: Set[marko.element.Element],
|
||||
parent_item: Optional[NodeItem] = None,
|
||||
):
|
||||
|
||||
if element in visited:
|
||||
return
|
||||
|
||||
# Iterates over all elements in the AST
|
||||
# Check for different element types and process relevant details
|
||||
if isinstance(element, marko.block.Heading) and len(element.children) > 0:
|
||||
self.close_table(doc)
|
||||
self.process_inline_text(parent_element, doc)
|
||||
self._close_table(doc)
|
||||
self._process_inline_text(parent_item, doc)
|
||||
_log.debug(
|
||||
f" - Heading level {element.level}, content: {element.children[0].children}" # type: ignore
|
||||
)
|
||||
@ -207,8 +212,8 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
traverse(element)
|
||||
snippet_text = "".join(strings)
|
||||
if len(snippet_text) > 0:
|
||||
parent_element = doc.add_text(
|
||||
label=doc_label, parent=parent_element, text=snippet_text
|
||||
parent_item = doc.add_text(
|
||||
label=doc_label, parent=parent_item, text=snippet_text
|
||||
)
|
||||
|
||||
elif isinstance(element, marko.block.List):
|
||||
@ -218,35 +223,37 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
has_non_empty_list_items = True
|
||||
break
|
||||
|
||||
self.close_table(doc)
|
||||
self.process_inline_text(parent_element, doc)
|
||||
self._close_table(doc)
|
||||
self._process_inline_text(parent_item, doc)
|
||||
_log.debug(f" - List {'ordered' if element.ordered else 'unordered'}")
|
||||
if has_non_empty_list_items:
|
||||
label = GroupLabel.ORDERED_LIST if element.ordered else GroupLabel.LIST
|
||||
parent_element = doc.add_group(
|
||||
label=label, name=f"list", parent=parent_element
|
||||
parent_item = doc.add_group(
|
||||
label=label, name=f"list", parent=parent_item
|
||||
)
|
||||
|
||||
elif isinstance(element, marko.block.ListItem) and len(element.children) > 0:
|
||||
self.close_table(doc)
|
||||
self.process_inline_text(parent_element, doc)
|
||||
self._close_table(doc)
|
||||
self._process_inline_text(parent_item, doc)
|
||||
_log.debug(" - List item")
|
||||
|
||||
snippet_text = str(element.children[0].children[0].children) # type: ignore
|
||||
first_child = element.children[0]
|
||||
snippet_text = str(first_child.children[0].children) # type: ignore
|
||||
is_numbered = False
|
||||
if (
|
||||
parent_element is not None
|
||||
and isinstance(parent_element, DocItem)
|
||||
and parent_element.label == GroupLabel.ORDERED_LIST
|
||||
parent_item is not None
|
||||
and isinstance(parent_item, DocItem)
|
||||
and parent_item.label == GroupLabel.ORDERED_LIST
|
||||
):
|
||||
is_numbered = True
|
||||
doc.add_list_item(
|
||||
enumerated=is_numbered, parent=parent_element, text=snippet_text
|
||||
enumerated=is_numbered, parent=parent_item, text=snippet_text
|
||||
)
|
||||
visited.add(first_child)
|
||||
|
||||
elif isinstance(element, marko.inline.Image):
|
||||
self.close_table(doc)
|
||||
self.process_inline_text(parent_element, doc)
|
||||
self._close_table(doc)
|
||||
self._process_inline_text(parent_item, doc)
|
||||
_log.debug(f" - Image with alt: {element.title}, url: {element.dest}")
|
||||
|
||||
fig_caption: Optional[TextItem] = None
|
||||
@ -255,10 +262,10 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
label=DocItemLabel.CAPTION, text=element.title
|
||||
)
|
||||
|
||||
doc.add_picture(parent=parent_element, caption=fig_caption)
|
||||
doc.add_picture(parent=parent_item, caption=fig_caption)
|
||||
|
||||
elif isinstance(element, marko.block.Paragraph) and len(element.children) > 0:
|
||||
self.process_inline_text(parent_element, doc)
|
||||
self._process_inline_text(parent_item, doc)
|
||||
|
||||
elif isinstance(element, marko.inline.RawText):
|
||||
_log.debug(f" - Paragraph (raw text): {element.children}")
|
||||
@ -272,17 +279,16 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
else:
|
||||
self.md_table_buffer.append(snippet_text)
|
||||
else:
|
||||
self.close_table(doc)
|
||||
self.in_table = False
|
||||
self._close_table(doc)
|
||||
# most likely just inline text
|
||||
self.inline_texts.append(str(element.children))
|
||||
|
||||
elif isinstance(element, marko.inline.CodeSpan):
|
||||
self.close_table(doc)
|
||||
self.process_inline_text(parent_element, doc)
|
||||
self._close_table(doc)
|
||||
self._process_inline_text(parent_item, doc)
|
||||
_log.debug(f" - Code Span: {element.children}")
|
||||
snippet_text = str(element.children).strip()
|
||||
doc.add_code(parent=parent_element, text=snippet_text)
|
||||
doc.add_code(parent=parent_item, text=snippet_text)
|
||||
|
||||
elif (
|
||||
isinstance(element, (marko.block.CodeBlock, marko.block.FencedCode))
|
||||
@ -290,10 +296,10 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
and isinstance((first_child := element.children[0]), marko.inline.RawText)
|
||||
and len(snippet_text := (first_child.children.strip())) > 0
|
||||
):
|
||||
self.close_table(doc)
|
||||
self.process_inline_text(parent_element, doc)
|
||||
self._close_table(doc)
|
||||
self._process_inline_text(parent_item, doc)
|
||||
_log.debug(f" - Code Block: {element.children}")
|
||||
doc.add_code(parent=parent_element, text=snippet_text)
|
||||
doc.add_code(parent=parent_item, text=snippet_text)
|
||||
|
||||
elif isinstance(element, marko.inline.LineBreak):
|
||||
if self.in_table:
|
||||
@ -302,8 +308,8 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
|
||||
elif isinstance(element, marko.block.HTMLBlock):
|
||||
self._html_blocks += 1
|
||||
self.process_inline_text(parent_element, doc)
|
||||
self.close_table(doc)
|
||||
self._process_inline_text(parent_item, doc)
|
||||
self._close_table(doc)
|
||||
_log.debug("HTML Block: {}".format(element))
|
||||
if (
|
||||
len(element.body) > 0
|
||||
@ -312,18 +318,16 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
|
||||
# wrap in markers to enable post-processing in convert()
|
||||
text_to_add = f"{_START_MARKER}{html_block}{_STOP_MARKER}"
|
||||
doc.add_code(parent=parent_element, text=text_to_add)
|
||||
doc.add_code(parent=parent_item, text=text_to_add)
|
||||
else:
|
||||
if not isinstance(element, str):
|
||||
self.close_table(doc)
|
||||
self._close_table(doc)
|
||||
_log.debug("Some other element: {}".format(element))
|
||||
|
||||
processed_block_types = (
|
||||
marko.block.ListItem,
|
||||
marko.block.Heading,
|
||||
marko.block.CodeBlock,
|
||||
marko.block.FencedCode,
|
||||
# marko.block.Paragraph,
|
||||
marko.inline.RawText,
|
||||
)
|
||||
|
||||
@ -332,7 +336,13 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
element, processed_block_types
|
||||
):
|
||||
for child in element.children:
|
||||
self.iterate_elements(child, depth + 1, doc, parent_element)
|
||||
self._iterate_elements(
|
||||
element=child,
|
||||
depth=depth + 1,
|
||||
doc=doc,
|
||||
visited=visited,
|
||||
parent_item=parent_item,
|
||||
)
|
||||
|
||||
def is_valid(self) -> bool:
|
||||
return self.valid
|
||||
@ -366,9 +376,15 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
marko_parser = Markdown()
|
||||
parsed_ast = marko_parser.parse(self.markdown)
|
||||
# Start iterating from the root of the AST
|
||||
self.iterate_elements(parsed_ast, 0, doc, None)
|
||||
self.process_inline_text(None, doc) # handle last hanging inline text
|
||||
self.close_table(doc=doc) # handle any last hanging table
|
||||
self._iterate_elements(
|
||||
element=parsed_ast,
|
||||
depth=0,
|
||||
doc=doc,
|
||||
parent_item=None,
|
||||
visited=set(),
|
||||
)
|
||||
self._process_inline_text(None, doc) # handle last hanging inline text
|
||||
self._close_table(doc=doc) # handle any last hanging table
|
||||
|
||||
# if HTML blocks were detected, export to HTML and delegate to HTML backend
|
||||
if self._html_blocks > 0:
|
||||
|
||||
@ -226,6 +226,10 @@ def convert(
|
||||
help="Enable the picture classification enrichment model in the pipeline.",
|
||||
),
|
||||
] = False,
|
||||
enrich_picture_description: Annotated[
|
||||
bool,
|
||||
typer.Option(..., help="Enable the picture description model in the pipeline."),
|
||||
] = False,
|
||||
artifacts_path: Annotated[
|
||||
Optional[Path],
|
||||
typer.Option(..., help="If provided, the location of the model artifacts."),
|
||||
@ -382,6 +386,7 @@ def convert(
|
||||
do_table_structure=True,
|
||||
do_code_enrichment=enrich_code,
|
||||
do_formula_enrichment=enrich_formula,
|
||||
do_picture_description=enrich_picture_description,
|
||||
do_picture_classification=enrich_picture_classes,
|
||||
document_timeout=document_timeout,
|
||||
)
|
||||
|
||||
@ -31,6 +31,7 @@ class _AvailableModels(str, Enum):
|
||||
TABLEFORMER = "tableformer"
|
||||
CODE_FORMULA = "code_formula"
|
||||
PICTURE_CLASSIFIER = "picture_classifier"
|
||||
SMOLVLM = "smolvlm"
|
||||
EASYOCR = "easyocr"
|
||||
|
||||
|
||||
@ -81,6 +82,7 @@ def download(
|
||||
with_tableformer=_AvailableModels.TABLEFORMER in to_download,
|
||||
with_code_formula=_AvailableModels.CODE_FORMULA in to_download,
|
||||
with_picture_classifier=_AvailableModels.PICTURE_CLASSIFIER in to_download,
|
||||
with_smolvlm=_AvailableModels.SMOLVLM in to_download,
|
||||
with_easyocr=_AvailableModels.EASYOCR in to_download,
|
||||
)
|
||||
|
||||
|
||||
@ -2,9 +2,9 @@ import logging
|
||||
import os
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from typing import Any, List, Literal, Optional, Union
|
||||
from typing import Annotated, Any, Dict, List, Literal, Optional, Union
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field, model_validator
|
||||
from pydantic import AnyUrl, BaseModel, ConfigDict, Field, model_validator
|
||||
from pydantic_settings import BaseSettings, SettingsConfigDict
|
||||
|
||||
_log = logging.getLogger(__name__)
|
||||
@ -184,6 +184,51 @@ class OcrMacOptions(OcrOptions):
|
||||
)
|
||||
|
||||
|
||||
class PictureDescriptionBaseOptions(BaseModel):
|
||||
kind: str
|
||||
batch_size: int = 8
|
||||
scale: float = 2
|
||||
|
||||
bitmap_area_threshold: float = (
|
||||
0.2 # percentage of the area for a bitmap to processed with the models
|
||||
)
|
||||
|
||||
|
||||
class PictureDescriptionApiOptions(PictureDescriptionBaseOptions):
|
||||
kind: Literal["api"] = "api"
|
||||
|
||||
url: AnyUrl = AnyUrl("http://localhost:8000/v1/chat/completions")
|
||||
headers: Dict[str, str] = {}
|
||||
params: Dict[str, Any] = {}
|
||||
timeout: float = 20
|
||||
|
||||
prompt: str = "Describe this image in a few sentences."
|
||||
provenance: str = ""
|
||||
|
||||
|
||||
class PictureDescriptionVlmOptions(PictureDescriptionBaseOptions):
|
||||
kind: Literal["vlm"] = "vlm"
|
||||
|
||||
repo_id: str
|
||||
prompt: str = "Describe this image in a few sentences."
|
||||
# Config from here https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.GenerationConfig
|
||||
generation_config: Dict[str, Any] = dict(max_new_tokens=200, do_sample=False)
|
||||
|
||||
@property
|
||||
def repo_cache_folder(self) -> str:
|
||||
return self.repo_id.replace("/", "--")
|
||||
|
||||
|
||||
smolvlm_picture_description = PictureDescriptionVlmOptions(
|
||||
repo_id="HuggingFaceTB/SmolVLM-256M-Instruct"
|
||||
)
|
||||
# phi_picture_description = PictureDescriptionVlmOptions(repo_id="microsoft/Phi-3-vision-128k-instruct")
|
||||
granite_picture_description = PictureDescriptionVlmOptions(
|
||||
repo_id="ibm-granite/granite-vision-3.1-2b-preview",
|
||||
prompt="What is shown in this image?",
|
||||
)
|
||||
|
||||
|
||||
# Define an enum for the backend options
|
||||
class PdfBackend(str, Enum):
|
||||
"""Enum of valid PDF backends."""
|
||||
@ -223,6 +268,7 @@ class PdfPipelineOptions(PipelineOptions):
|
||||
do_code_enrichment: bool = False # True: perform code OCR
|
||||
do_formula_enrichment: bool = False # True: perform formula OCR, return Latex code
|
||||
do_picture_classification: bool = False # True: classify pictures in documents
|
||||
do_picture_description: bool = False # True: run describe pictures in documents
|
||||
|
||||
table_structure_options: TableStructureOptions = TableStructureOptions()
|
||||
ocr_options: Union[
|
||||
@ -232,6 +278,10 @@ class PdfPipelineOptions(PipelineOptions):
|
||||
OcrMacOptions,
|
||||
RapidOcrOptions,
|
||||
] = Field(EasyOcrOptions(), discriminator="kind")
|
||||
picture_description_options: Annotated[
|
||||
Union[PictureDescriptionApiOptions, PictureDescriptionVlmOptions],
|
||||
Field(discriminator="kind"),
|
||||
] = smolvlm_picture_description
|
||||
|
||||
images_scale: float = 1.0
|
||||
generate_page_images: bool = False
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Generic, Iterable, Optional
|
||||
|
||||
from docling_core.types.doc import BoundingBox, DoclingDocument, NodeItem, TextItem
|
||||
from docling_core.types.doc import BoundingBox, DocItem, DoclingDocument, NodeItem
|
||||
from typing_extensions import TypeVar
|
||||
|
||||
from docling.datamodel.base_models import ItemAndImageEnrichmentElement, Page
|
||||
@ -64,7 +64,7 @@ class BaseItemAndImageEnrichmentModel(
|
||||
if not self.is_processable(doc=conv_res.document, element=element):
|
||||
return None
|
||||
|
||||
assert isinstance(element, TextItem)
|
||||
assert isinstance(element, DocItem)
|
||||
element_prov = element.prov[0]
|
||||
|
||||
bbox = element_prov.bbox
|
||||
|
||||
@ -62,7 +62,7 @@ class CodeFormulaModel(BaseItemAndImageEnrichmentModel):
|
||||
Processes the given batch of elements and enriches them with predictions.
|
||||
"""
|
||||
|
||||
_model_repo_folder = "CodeFormula"
|
||||
_model_repo_folder = "ds4sd--CodeFormula"
|
||||
elements_batch_size = 5
|
||||
images_scale = 1.66 # = 120 dpi, aligned with training data resolution
|
||||
expansion_factor = 0.03
|
||||
|
||||
@ -56,7 +56,7 @@ class DocumentPictureClassifier(BaseEnrichmentModel):
|
||||
Processes a batch of elements and adds classification annotations.
|
||||
"""
|
||||
|
||||
_model_repo_folder = "DocumentFigureClassifier"
|
||||
_model_repo_folder = "ds4sd--DocumentFigureClassifier"
|
||||
images_scale = 2
|
||||
|
||||
def __init__(
|
||||
|
||||
@ -4,9 +4,7 @@ import zipfile
|
||||
from pathlib import Path
|
||||
from typing import Iterable, List, Optional
|
||||
|
||||
import httpx
|
||||
import numpy
|
||||
import torch
|
||||
from docling_core.types.doc import BoundingBox, CoordOrigin
|
||||
|
||||
from docling.datamodel.base_models import Cell, OcrCell, Page
|
||||
|
||||
@ -22,7 +22,7 @@ _log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LayoutModel(BasePageModel):
|
||||
_model_repo_folder = "docling-models"
|
||||
_model_repo_folder = "ds4sd--docling-models"
|
||||
_model_path = "model_artifacts/layout"
|
||||
|
||||
TEXT_ELEM_LABELS = [
|
||||
|
||||
101
docling/models/picture_description_api_model.py
Normal file
101
docling/models/picture_description_api_model.py
Normal file
@ -0,0 +1,101 @@
|
||||
import base64
|
||||
import io
|
||||
import logging
|
||||
from typing import Iterable, List, Optional
|
||||
|
||||
import requests
|
||||
from PIL import Image
|
||||
from pydantic import BaseModel, ConfigDict
|
||||
|
||||
from docling.datamodel.pipeline_options import PictureDescriptionApiOptions
|
||||
from docling.models.picture_description_base_model import PictureDescriptionBaseModel
|
||||
|
||||
_log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ChatMessage(BaseModel):
|
||||
role: str
|
||||
content: str
|
||||
|
||||
|
||||
class ResponseChoice(BaseModel):
|
||||
index: int
|
||||
message: ChatMessage
|
||||
finish_reason: str
|
||||
|
||||
|
||||
class ResponseUsage(BaseModel):
|
||||
prompt_tokens: int
|
||||
completion_tokens: int
|
||||
total_tokens: int
|
||||
|
||||
|
||||
class ApiResponse(BaseModel):
|
||||
model_config = ConfigDict(
|
||||
protected_namespaces=(),
|
||||
)
|
||||
|
||||
id: str
|
||||
model: Optional[str] = None # returned by openai
|
||||
choices: List[ResponseChoice]
|
||||
created: int
|
||||
usage: ResponseUsage
|
||||
|
||||
|
||||
class PictureDescriptionApiModel(PictureDescriptionBaseModel):
|
||||
# elements_batch_size = 4
|
||||
|
||||
def __init__(self, enabled: bool, options: PictureDescriptionApiOptions):
|
||||
super().__init__(enabled=enabled, options=options)
|
||||
self.options: PictureDescriptionApiOptions
|
||||
|
||||
if self.enabled:
|
||||
if options.url.host != "localhost":
|
||||
raise NotImplementedError(
|
||||
"The options try to connect to remote APIs which are not yet allowed."
|
||||
)
|
||||
|
||||
def _annotate_images(self, images: Iterable[Image.Image]) -> Iterable[str]:
|
||||
# Note: technically we could make a batch request here,
|
||||
# but not all APIs will allow for it. For example, vllm won't allow more than 1.
|
||||
for image in images:
|
||||
img_io = io.BytesIO()
|
||||
image.save(img_io, "PNG")
|
||||
image_base64 = base64.b64encode(img_io.getvalue()).decode("utf-8")
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": self.options.prompt,
|
||||
},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": f"data:image/png;base64,{image_base64}"
|
||||
},
|
||||
},
|
||||
],
|
||||
}
|
||||
]
|
||||
|
||||
payload = {
|
||||
"messages": messages,
|
||||
**self.options.params,
|
||||
}
|
||||
|
||||
r = requests.post(
|
||||
str(self.options.url),
|
||||
headers=self.options.headers,
|
||||
json=payload,
|
||||
timeout=self.options.timeout,
|
||||
)
|
||||
if not r.ok:
|
||||
_log.error(f"Error calling the API. Reponse was {r.text}")
|
||||
r.raise_for_status()
|
||||
|
||||
api_resp = ApiResponse.model_validate_json(r.text)
|
||||
generated_text = api_resp.choices[0].message.content.strip()
|
||||
yield generated_text
|
||||
64
docling/models/picture_description_base_model.py
Normal file
64
docling/models/picture_description_base_model.py
Normal file
@ -0,0 +1,64 @@
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Any, Iterable, List, Optional, Union
|
||||
|
||||
from docling_core.types.doc import (
|
||||
DoclingDocument,
|
||||
NodeItem,
|
||||
PictureClassificationClass,
|
||||
PictureItem,
|
||||
)
|
||||
from docling_core.types.doc.document import ( # TODO: move import to docling_core.types.doc
|
||||
PictureDescriptionData,
|
||||
)
|
||||
from PIL import Image
|
||||
|
||||
from docling.datamodel.pipeline_options import PictureDescriptionBaseOptions
|
||||
from docling.models.base_model import (
|
||||
BaseItemAndImageEnrichmentModel,
|
||||
ItemAndImageEnrichmentElement,
|
||||
)
|
||||
|
||||
|
||||
class PictureDescriptionBaseModel(BaseItemAndImageEnrichmentModel):
|
||||
images_scale: float = 2.0
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
enabled: bool,
|
||||
options: PictureDescriptionBaseOptions,
|
||||
):
|
||||
self.enabled = enabled
|
||||
self.options = options
|
||||
self.provenance = "not-implemented"
|
||||
|
||||
def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool:
|
||||
return self.enabled and isinstance(element, PictureItem)
|
||||
|
||||
def _annotate_images(self, images: Iterable[Image.Image]) -> Iterable[str]:
|
||||
raise NotImplementedError
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
doc: DoclingDocument,
|
||||
element_batch: Iterable[ItemAndImageEnrichmentElement],
|
||||
) -> Iterable[NodeItem]:
|
||||
if not self.enabled:
|
||||
for element in element_batch:
|
||||
yield element.item
|
||||
return
|
||||
|
||||
images: List[Image.Image] = []
|
||||
elements: List[PictureItem] = []
|
||||
for el in element_batch:
|
||||
assert isinstance(el.item, PictureItem)
|
||||
elements.append(el.item)
|
||||
images.append(el.image)
|
||||
|
||||
outputs = self._annotate_images(images)
|
||||
|
||||
for item, output in zip(elements, outputs):
|
||||
item.annotations.append(
|
||||
PictureDescriptionData(text=output, provenance=self.provenance)
|
||||
)
|
||||
yield item
|
||||
109
docling/models/picture_description_vlm_model.py
Normal file
109
docling/models/picture_description_vlm_model.py
Normal file
@ -0,0 +1,109 @@
|
||||
from pathlib import Path
|
||||
from typing import Iterable, Optional, Union
|
||||
|
||||
from PIL import Image
|
||||
|
||||
from docling.datamodel.pipeline_options import (
|
||||
AcceleratorOptions,
|
||||
PictureDescriptionVlmOptions,
|
||||
)
|
||||
from docling.models.picture_description_base_model import PictureDescriptionBaseModel
|
||||
from docling.utils.accelerator_utils import decide_device
|
||||
|
||||
|
||||
class PictureDescriptionVlmModel(PictureDescriptionBaseModel):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
enabled: bool,
|
||||
artifacts_path: Optional[Union[Path, str]],
|
||||
options: PictureDescriptionVlmOptions,
|
||||
accelerator_options: AcceleratorOptions,
|
||||
):
|
||||
super().__init__(enabled=enabled, options=options)
|
||||
self.options: PictureDescriptionVlmOptions
|
||||
|
||||
if self.enabled:
|
||||
|
||||
if artifacts_path is None:
|
||||
artifacts_path = self.download_models(repo_id=self.options.repo_id)
|
||||
else:
|
||||
artifacts_path = Path(artifacts_path) / self.options.repo_cache_folder
|
||||
|
||||
self.device = decide_device(accelerator_options.device)
|
||||
|
||||
try:
|
||||
import torch
|
||||
from transformers import AutoModelForVision2Seq, AutoProcessor
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"transformers >=4.46 is not installed. Please install Docling with the required extras `pip install docling[vlm]`."
|
||||
)
|
||||
|
||||
# Initialize processor and model
|
||||
self.processor = AutoProcessor.from_pretrained(self.options.repo_id)
|
||||
self.model = AutoModelForVision2Seq.from_pretrained(
|
||||
self.options.repo_id,
|
||||
torch_dtype=torch.bfloat16,
|
||||
_attn_implementation=(
|
||||
"flash_attention_2" if self.device.startswith("cuda") else "eager"
|
||||
),
|
||||
).to(self.device)
|
||||
|
||||
self.provenance = f"{self.options.repo_id}"
|
||||
|
||||
@staticmethod
|
||||
def download_models(
|
||||
repo_id: str,
|
||||
local_dir: Optional[Path] = None,
|
||||
force: bool = False,
|
||||
progress: bool = False,
|
||||
) -> Path:
|
||||
from huggingface_hub import snapshot_download
|
||||
from huggingface_hub.utils import disable_progress_bars
|
||||
|
||||
if not progress:
|
||||
disable_progress_bars()
|
||||
download_path = snapshot_download(
|
||||
repo_id=repo_id,
|
||||
force_download=force,
|
||||
local_dir=local_dir,
|
||||
)
|
||||
|
||||
return Path(download_path)
|
||||
|
||||
def _annotate_images(self, images: Iterable[Image.Image]) -> Iterable[str]:
|
||||
from transformers import GenerationConfig
|
||||
|
||||
# Create input messages
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image"},
|
||||
{"type": "text", "text": self.options.prompt},
|
||||
],
|
||||
},
|
||||
]
|
||||
|
||||
# TODO: do batch generation
|
||||
|
||||
for image in images:
|
||||
# Prepare inputs
|
||||
prompt = self.processor.apply_chat_template(
|
||||
messages, add_generation_prompt=True
|
||||
)
|
||||
inputs = self.processor(text=prompt, images=[image], return_tensors="pt")
|
||||
inputs = inputs.to(self.device)
|
||||
|
||||
# Generate outputs
|
||||
generated_ids = self.model.generate(
|
||||
**inputs,
|
||||
generation_config=GenerationConfig(**self.options.generation_config),
|
||||
)
|
||||
generated_texts = self.processor.batch_decode(
|
||||
generated_ids[:, inputs["input_ids"].shape[1] :],
|
||||
skip_special_tokens=True,
|
||||
)
|
||||
|
||||
yield generated_texts[0].strip()
|
||||
@ -23,7 +23,7 @@ from docling.utils.profiling import TimeRecorder
|
||||
|
||||
|
||||
class TableStructureModel(BasePageModel):
|
||||
_model_repo_folder = "docling-models"
|
||||
_model_repo_folder = "ds4sd--docling-models"
|
||||
_model_path = "model_artifacts/tableformer"
|
||||
|
||||
def __init__(
|
||||
|
||||
@ -14,6 +14,8 @@ from docling.datamodel.pipeline_options import (
|
||||
EasyOcrOptions,
|
||||
OcrMacOptions,
|
||||
PdfPipelineOptions,
|
||||
PictureDescriptionApiOptions,
|
||||
PictureDescriptionVlmOptions,
|
||||
RapidOcrOptions,
|
||||
TesseractCliOcrOptions,
|
||||
TesseractOcrOptions,
|
||||
@ -34,6 +36,9 @@ from docling.models.page_preprocessing_model import (
|
||||
PagePreprocessingModel,
|
||||
PagePreprocessingOptions,
|
||||
)
|
||||
from docling.models.picture_description_api_model import PictureDescriptionApiModel
|
||||
from docling.models.picture_description_base_model import PictureDescriptionBaseModel
|
||||
from docling.models.picture_description_vlm_model import PictureDescriptionVlmModel
|
||||
from docling.models.rapid_ocr_model import RapidOcrModel
|
||||
from docling.models.table_structure_model import TableStructureModel
|
||||
from docling.models.tesseract_ocr_cli_model import TesseractOcrCliModel
|
||||
@ -95,8 +100,17 @@ class StandardPdfPipeline(PaginatedPipeline):
|
||||
PageAssembleModel(options=PageAssembleOptions()),
|
||||
]
|
||||
|
||||
# Picture description model
|
||||
if (
|
||||
picture_description_model := self.get_picture_description_model(
|
||||
artifacts_path=artifacts_path
|
||||
)
|
||||
) is None:
|
||||
raise RuntimeError(
|
||||
f"The specified picture description kind is not supported: {pipeline_options.picture_description_options.kind}."
|
||||
)
|
||||
|
||||
self.enrichment_pipe = [
|
||||
# Other models working on `NodeItem` elements in the DoclingDocument
|
||||
# Code Formula Enrichment Model
|
||||
CodeFormulaModel(
|
||||
enabled=pipeline_options.do_code_enrichment
|
||||
@ -115,11 +129,14 @@ class StandardPdfPipeline(PaginatedPipeline):
|
||||
options=DocumentPictureClassifierOptions(),
|
||||
accelerator_options=pipeline_options.accelerator_options,
|
||||
),
|
||||
# Document Picture description
|
||||
picture_description_model,
|
||||
]
|
||||
|
||||
if (
|
||||
self.pipeline_options.do_formula_enrichment
|
||||
or self.pipeline_options.do_code_enrichment
|
||||
or self.pipeline_options.do_picture_description
|
||||
):
|
||||
self.keep_backend = True
|
||||
|
||||
@ -175,6 +192,29 @@ class StandardPdfPipeline(PaginatedPipeline):
|
||||
)
|
||||
return None
|
||||
|
||||
def get_picture_description_model(
|
||||
self, artifacts_path: Optional[Path] = None
|
||||
) -> Optional[PictureDescriptionBaseModel]:
|
||||
if isinstance(
|
||||
self.pipeline_options.picture_description_options,
|
||||
PictureDescriptionApiOptions,
|
||||
):
|
||||
return PictureDescriptionApiModel(
|
||||
enabled=self.pipeline_options.do_picture_description,
|
||||
options=self.pipeline_options.picture_description_options,
|
||||
)
|
||||
elif isinstance(
|
||||
self.pipeline_options.picture_description_options,
|
||||
PictureDescriptionVlmOptions,
|
||||
):
|
||||
return PictureDescriptionVlmModel(
|
||||
enabled=self.pipeline_options.do_picture_description,
|
||||
artifacts_path=artifacts_path,
|
||||
options=self.pipeline_options.picture_description_options,
|
||||
accelerator_options=self.pipeline_options.accelerator_options,
|
||||
)
|
||||
return None
|
||||
|
||||
def initialize_page(self, conv_res: ConversionResult, page: Page) -> Page:
|
||||
with TimeRecorder(conv_res, "page_init"):
|
||||
page._backend = conv_res.input._backend.load_page(page.page_no) # type: ignore
|
||||
|
||||
@ -2,11 +2,13 @@ import logging
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from docling.datamodel.pipeline_options import smolvlm_picture_description
|
||||
from docling.datamodel.settings import settings
|
||||
from docling.models.code_formula_model import CodeFormulaModel
|
||||
from docling.models.document_picture_classifier import DocumentPictureClassifier
|
||||
from docling.models.easyocr_model import EasyOcrModel
|
||||
from docling.models.layout_model import LayoutModel
|
||||
from docling.models.picture_description_vlm_model import PictureDescriptionVlmModel
|
||||
from docling.models.table_structure_model import TableStructureModel
|
||||
|
||||
_log = logging.getLogger(__name__)
|
||||
@ -21,6 +23,7 @@ def download_models(
|
||||
with_tableformer: bool = True,
|
||||
with_code_formula: bool = True,
|
||||
with_picture_classifier: bool = True,
|
||||
with_smolvlm: bool = True,
|
||||
with_easyocr: bool = True,
|
||||
):
|
||||
if output_dir is None:
|
||||
@ -61,6 +64,15 @@ def download_models(
|
||||
progress=progress,
|
||||
)
|
||||
|
||||
if with_smolvlm:
|
||||
_log.info(f"Downloading SmolVlm model...")
|
||||
PictureDescriptionVlmModel.download_models(
|
||||
repo_id=smolvlm_picture_description.repo_id,
|
||||
local_dir=output_dir / smolvlm_picture_description.repo_cache_folder,
|
||||
force=force,
|
||||
progress=progress,
|
||||
)
|
||||
|
||||
if with_easyocr:
|
||||
_log.info(f"Downloading easyocr models...")
|
||||
EasyOcrModel.download_models(
|
||||
|
||||
48
docs/examples/pictures_description.py
Normal file
48
docs/examples/pictures_description.py
Normal file
@ -0,0 +1,48 @@
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
from docling_core.types.doc import PictureItem
|
||||
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.pipeline_options import (
|
||||
PdfPipelineOptions,
|
||||
granite_picture_description,
|
||||
smolvlm_picture_description,
|
||||
)
|
||||
from docling.document_converter import DocumentConverter, PdfFormatOption
|
||||
|
||||
|
||||
def main():
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")
|
||||
|
||||
pipeline_options = PdfPipelineOptions()
|
||||
pipeline_options.do_picture_description = True
|
||||
pipeline_options.picture_description_options = smolvlm_picture_description
|
||||
# pipeline_options.picture_description_options = granite_picture_description
|
||||
|
||||
pipeline_options.picture_description_options.prompt = (
|
||||
"Describe the image in three sentences. Be consise and accurate."
|
||||
)
|
||||
|
||||
doc_converter = DocumentConverter(
|
||||
format_options={
|
||||
InputFormat.PDF: PdfFormatOption(
|
||||
pipeline_options=pipeline_options,
|
||||
)
|
||||
}
|
||||
)
|
||||
result = doc_converter.convert(input_doc_path)
|
||||
|
||||
for element, _level in result.document.iterate_items():
|
||||
if isinstance(element, PictureItem):
|
||||
print(
|
||||
f"Picture {element.self_ref}\n"
|
||||
f"Caption: {element.caption_text(doc=result.document)}\n"
|
||||
f"Annotations: {element.annotations}"
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
55
docs/examples/pictures_description_api.py
Normal file
55
docs/examples/pictures_description_api.py
Normal file
@ -0,0 +1,55 @@
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
from docling_core.types.doc import PictureItem
|
||||
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.pipeline_options import (
|
||||
PdfPipelineOptions,
|
||||
PictureDescriptionApiOptions,
|
||||
)
|
||||
from docling.document_converter import DocumentConverter, PdfFormatOption
|
||||
|
||||
|
||||
def main():
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")
|
||||
|
||||
# This is using a local API server to do picture description.
|
||||
# For example, you can launch it locally with:
|
||||
# $ vllm serve "HuggingFaceTB/SmolVLM-256M-Instruct"
|
||||
|
||||
pipeline_options = PdfPipelineOptions()
|
||||
pipeline_options.do_picture_description = True
|
||||
pipeline_options.picture_description_options = PictureDescriptionApiOptions(
|
||||
url="http://localhost:8000/v1/chat/completions",
|
||||
params=dict(
|
||||
model="HuggingFaceTB/SmolVLM-256M-Instruct",
|
||||
seed=42,
|
||||
max_completion_tokens=200,
|
||||
),
|
||||
prompt="Describe the image in three sentences. Be consise and accurate.",
|
||||
timeout=90,
|
||||
)
|
||||
|
||||
doc_converter = DocumentConverter(
|
||||
format_options={
|
||||
InputFormat.PDF: PdfFormatOption(
|
||||
pipeline_options=pipeline_options,
|
||||
)
|
||||
}
|
||||
)
|
||||
result = doc_converter.convert(input_doc_path)
|
||||
|
||||
for element, _level in result.document.iterate_items():
|
||||
if isinstance(element, PictureItem):
|
||||
print(
|
||||
f"Picture {element.self_ref}\n"
|
||||
f"Caption: {element.caption_text(doc=result.document)}\n"
|
||||
f"Annotations: {element.annotations}"
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
299
poetry.lock
generated
299
poetry.lock
generated
@ -2,13 +2,13 @@
|
||||
|
||||
[[package]]
|
||||
name = "aiohappyeyeballs"
|
||||
version = "2.4.4"
|
||||
version = "2.4.6"
|
||||
description = "Happy Eyeballs for asyncio"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"},
|
||||
{file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"},
|
||||
{file = "aiohappyeyeballs-2.4.6-py3-none-any.whl", hash = "sha256:147ec992cf873d74f5062644332c539fcd42956dc69453fe5204195e560517e1"},
|
||||
{file = "aiohappyeyeballs-2.4.6.tar.gz", hash = "sha256:9b05052f9042985d32ecbe4b59a77ae19c006a78f1344d7fdad69d28ded3d0b0"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -1544,13 +1544,13 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
|
||||
|
||||
[[package]]
|
||||
name = "identify"
|
||||
version = "2.6.6"
|
||||
version = "2.6.7"
|
||||
description = "File identification library for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "identify-2.6.6-py2.py3-none-any.whl", hash = "sha256:cbd1810bce79f8b671ecb20f53ee0ae8e86ae84b557de31d89709dc2a48ba881"},
|
||||
{file = "identify-2.6.6.tar.gz", hash = "sha256:7bec12768ed44ea4761efb47806f0a41f86e7c0a5fdf5950d4648c90eca7e251"},
|
||||
{file = "identify-2.6.7-py2.py3-none-any.whl", hash = "sha256:155931cb617a401807b09ecec6635d6c692d180090a1cedca8ef7d58ba5b6aa0"},
|
||||
{file = "identify-2.6.7.tar.gz", hash = "sha256:3fa266b42eba321ee0b2bb0936a6a6b9e36a1351cbb69055b3082f4193035684"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@ -2253,157 +2253,157 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "lxml"
|
||||
version = "5.3.0"
|
||||
version = "5.3.1"
|
||||
description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "lxml-5.3.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:dd36439be765e2dde7660212b5275641edbc813e7b24668831a5c8ac91180656"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ae5fe5c4b525aa82b8076c1a59d642c17b6e8739ecf852522c6321852178119d"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:501d0d7e26b4d261fca8132854d845e4988097611ba2531408ec91cf3fd9d20a"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fb66442c2546446944437df74379e9cf9e9db353e61301d1a0e26482f43f0dd8"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9e41506fec7a7f9405b14aa2d5c8abbb4dbbd09d88f9496958b6d00cb4d45330"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f7d4a670107d75dfe5ad080bed6c341d18c4442f9378c9f58e5851e86eb79965"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:41ce1f1e2c7755abfc7e759dc34d7d05fd221723ff822947132dc934d122fe22"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:44264ecae91b30e5633013fb66f6ddd05c006d3e0e884f75ce0b4755b3e3847b"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-manylinux_2_28_ppc64le.whl", hash = "sha256:3c174dc350d3ec52deb77f2faf05c439331d6ed5e702fc247ccb4e6b62d884b7"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-manylinux_2_28_s390x.whl", hash = "sha256:2dfab5fa6a28a0b60a20638dc48e6343c02ea9933e3279ccb132f555a62323d8"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:b1c8c20847b9f34e98080da785bb2336ea982e7f913eed5809e5a3c872900f32"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:2c86bf781b12ba417f64f3422cfc302523ac9cd1d8ae8c0f92a1c66e56ef2e86"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:c162b216070f280fa7da844531169be0baf9ccb17263cf5a8bf876fcd3117fa5"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:36aef61a1678cb778097b4a6eeae96a69875d51d1e8f4d4b491ab3cfb54b5a03"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:f65e5120863c2b266dbcc927b306c5b78e502c71edf3295dfcb9501ec96e5fc7"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-win32.whl", hash = "sha256:ef0c1fe22171dd7c7c27147f2e9c3e86f8bdf473fed75f16b0c2e84a5030ce80"},
|
||||
{file = "lxml-5.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:052d99051e77a4f3e8482c65014cf6372e61b0a6f4fe9edb98503bb5364cfee3"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:74bcb423462233bc5d6066e4e98b0264e7c1bed7541fff2f4e34fe6b21563c8b"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a3d819eb6f9b8677f57f9664265d0a10dd6551d227afb4af2b9cd7bdc2ccbf18"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5b8f5db71b28b8c404956ddf79575ea77aa8b1538e8b2ef9ec877945b3f46442"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2c3406b63232fc7e9b8783ab0b765d7c59e7c59ff96759d8ef9632fca27c7ee4"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2ecdd78ab768f844c7a1d4a03595038c166b609f6395e25af9b0f3f26ae1230f"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:168f2dfcfdedf611eb285efac1516c8454c8c99caf271dccda8943576b67552e"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aa617107a410245b8660028a7483b68e7914304a6d4882b5ff3d2d3eb5948d8c"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:69959bd3167b993e6e710b99051265654133a98f20cec1d9b493b931942e9c16"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-manylinux_2_28_ppc64le.whl", hash = "sha256:bd96517ef76c8654446fc3db9242d019a1bb5fe8b751ba414765d59f99210b79"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-manylinux_2_28_s390x.whl", hash = "sha256:ab6dd83b970dc97c2d10bc71aa925b84788c7c05de30241b9e96f9b6d9ea3080"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:eec1bb8cdbba2925bedc887bc0609a80e599c75b12d87ae42ac23fd199445654"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:6a7095eeec6f89111d03dabfe5883a1fd54da319c94e0fb104ee8f23616b572d"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:6f651ebd0b21ec65dfca93aa629610a0dbc13dbc13554f19b0113da2e61a4763"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:f422a209d2455c56849442ae42f25dbaaba1c6c3f501d58761c619c7836642ec"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:62f7fdb0d1ed2065451f086519865b4c90aa19aed51081979ecd05a21eb4d1be"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-win32.whl", hash = "sha256:c6379f35350b655fd817cd0d6cbeef7f265f3ae5fedb1caae2eb442bbeae9ab9"},
|
||||
{file = "lxml-5.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:9c52100e2c2dbb0649b90467935c4b0de5528833c76a35ea1a2691ec9f1ee7a1"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:e99f5507401436fdcc85036a2e7dc2e28d962550afe1cbfc07c40e454256a859"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:384aacddf2e5813a36495233b64cb96b1949da72bef933918ba5c84e06af8f0e"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:874a216bf6afaf97c263b56371434e47e2c652d215788396f60477540298218f"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:65ab5685d56914b9a2a34d67dd5488b83213d680b0c5d10b47f81da5a16b0b0e"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aac0bbd3e8dd2d9c45ceb82249e8bdd3ac99131a32b4d35c8af3cc9db1657179"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b369d3db3c22ed14c75ccd5af429086f166a19627e84a8fdade3f8f31426e52a"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c24037349665434f375645fa9d1f5304800cec574d0310f618490c871fd902b3"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:62d172f358f33a26d6b41b28c170c63886742f5b6772a42b59b4f0fa10526cb1"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-manylinux_2_28_ppc64le.whl", hash = "sha256:c1f794c02903c2824fccce5b20c339a1a14b114e83b306ff11b597c5f71a1c8d"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-manylinux_2_28_s390x.whl", hash = "sha256:5d6a6972b93c426ace71e0be9a6f4b2cfae9b1baed2eed2006076a746692288c"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:3879cc6ce938ff4eb4900d901ed63555c778731a96365e53fadb36437a131a99"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:74068c601baff6ff021c70f0935b0c7bc528baa8ea210c202e03757c68c5a4ff"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:ecd4ad8453ac17bc7ba3868371bffb46f628161ad0eefbd0a855d2c8c32dd81a"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:7e2f58095acc211eb9d8b5771bf04df9ff37d6b87618d1cbf85f92399c98dae8"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e63601ad5cd8f860aa99d109889b5ac34de571c7ee902d6812d5d9ddcc77fa7d"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-win32.whl", hash = "sha256:17e8d968d04a37c50ad9c456a286b525d78c4a1c15dd53aa46c1d8e06bf6fa30"},
|
||||
{file = "lxml-5.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:c1a69e58a6bb2de65902051d57fde951febad631a20a64572677a1052690482f"},
|
||||
{file = "lxml-5.3.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:8c72e9563347c7395910de6a3100a4840a75a6f60e05af5e58566868d5eb2d6a"},
|
||||
{file = "lxml-5.3.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e92ce66cd919d18d14b3856906a61d3f6b6a8500e0794142338da644260595cd"},
|
||||
{file = "lxml-5.3.0-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1d04f064bebdfef9240478f7a779e8c5dc32b8b7b0b2fc6a62e39b928d428e51"},
|
||||
{file = "lxml-5.3.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5c2fb570d7823c2bbaf8b419ba6e5662137f8166e364a8b2b91051a1fb40ab8b"},
|
||||
{file = "lxml-5.3.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0c120f43553ec759f8de1fee2f4794452b0946773299d44c36bfe18e83caf002"},
|
||||
{file = "lxml-5.3.0-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:562e7494778a69086f0312ec9689f6b6ac1c6b65670ed7d0267e49f57ffa08c4"},
|
||||
{file = "lxml-5.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:423b121f7e6fa514ba0c7918e56955a1d4470ed35faa03e3d9f0e3baa4c7e492"},
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{file = "lxml-5.3.1-cp39-cp39-manylinux_2_28_s390x.whl", hash = "sha256:136bf638d92848a939fd8f0e06fcf92d9f2e4b57969d94faae27c55f3d85c05b"},
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{file = "lxml-5.3.1-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:89934f9f791566e54c1d92cdc8f8fd0009447a5ecdb1ec6b810d5f8c4955f6be"},
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{file = "lxml-5.3.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:a8ade0363f776f87f982572c2860cc43c65ace208db49c76df0a21dde4ddd16e"},
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||||
{file = "lxml-5.3.1-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:bfbbab9316330cf81656fed435311386610f78b6c93cc5db4bebbce8dd146675"},
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||||
{file = "lxml-5.3.1-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:172d65f7c72a35a6879217bcdb4bb11bc88d55fb4879e7569f55616062d387c2"},
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||||
{file = "lxml-5.3.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:e3c623923967f3e5961d272718655946e5322b8d058e094764180cdee7bab1af"},
|
||||
{file = "lxml-5.3.1-cp39-cp39-win32.whl", hash = "sha256:ce0930a963ff593e8bb6fda49a503911accc67dee7e5445eec972668e672a0f0"},
|
||||
{file = "lxml-5.3.1-cp39-cp39-win_amd64.whl", hash = "sha256:f7b64fcd670bca8800bc10ced36620c6bbb321e7bc1214b9c0c0df269c1dddc2"},
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||||
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|
||||
{file = "lxml-5.3.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:67f5e80adf0aafc7b5454f2c1cb0cde920c9b1f2cbd0485f07cc1d0497c35c5d"},
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{file = "lxml-5.3.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2dd0b80ac2d8f13ffc906123a6f20b459cb50a99222d0da492360512f3e50f84"},
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||||
{file = "lxml-5.3.1-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:422c179022ecdedbe58b0e242607198580804253da220e9454ffe848daa1cfd2"},
|
||||
{file = "lxml-5.3.1-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:524ccfded8989a6595dbdda80d779fb977dbc9a7bc458864fc9a0c2fc15dc877"},
|
||||
{file = "lxml-5.3.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:48fd46bf7155def2e15287c6f2b133a2f78e2d22cdf55647269977b873c65499"},
|
||||
{file = "lxml-5.3.1-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:05123fad495a429f123307ac6d8fd6f977b71e9a0b6d9aeeb8f80c017cb17131"},
|
||||
{file = "lxml-5.3.1-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a243132767150a44e6a93cd1dde41010036e1cbc63cc3e9fe1712b277d926ce3"},
|
||||
{file = "lxml-5.3.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c92ea6d9dd84a750b2bae72ff5e8cf5fdd13e58dda79c33e057862c29a8d5b50"},
|
||||
{file = "lxml-5.3.1-pp37-pypy37_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:2f1be45d4c15f237209bbf123a0e05b5d630c8717c42f59f31ea9eae2ad89394"},
|
||||
{file = "lxml-5.3.1-pp37-pypy37_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:a83d3adea1e0ee36dac34627f78ddd7f093bb9cfc0a8e97f1572a949b695cb98"},
|
||||
{file = "lxml-5.3.1-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:3edbb9c9130bac05d8c3fe150c51c337a471cc7fdb6d2a0a7d3a88e88a829314"},
|
||||
{file = "lxml-5.3.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:2f23cf50eccb3255b6e913188291af0150d89dab44137a69e14e4dcb7be981f1"},
|
||||
{file = "lxml-5.3.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:df7e5edac4778127f2bf452e0721a58a1cfa4d1d9eac63bdd650535eb8543615"},
|
||||
{file = "lxml-5.3.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:094b28ed8a8a072b9e9e2113a81fda668d2053f2ca9f2d202c2c8c7c2d6516b1"},
|
||||
{file = "lxml-5.3.1-pp38-pypy38_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:514fe78fc4b87e7a7601c92492210b20a1b0c6ab20e71e81307d9c2e377c64de"},
|
||||
{file = "lxml-5.3.1-pp38-pypy38_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:8fffc08de02071c37865a155e5ea5fce0282e1546fd5bde7f6149fcaa32558ac"},
|
||||
{file = "lxml-5.3.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:4b0d5cdba1b655d5b18042ac9c9ff50bda33568eb80feaaca4fc237b9c4fbfde"},
|
||||
{file = "lxml-5.3.1-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:3031e4c16b59424e8d78522c69b062d301d951dc55ad8685736c3335a97fc270"},
|
||||
{file = "lxml-5.3.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cb659702a45136c743bc130760c6f137870d4df3a9e14386478b8a0511abcfca"},
|
||||
{file = "lxml-5.3.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5a11b16a33656ffc43c92a5343a28dc71eefe460bcc2a4923a96f292692709f6"},
|
||||
{file = "lxml-5.3.1-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:c5ae125276f254b01daa73e2c103363d3e99e3e10505686ac7d9d2442dd4627a"},
|
||||
{file = "lxml-5.3.1-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:c76722b5ed4a31ba103e0dc77ab869222ec36efe1a614e42e9bcea88a36186fe"},
|
||||
{file = "lxml-5.3.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:33e06717c00c788ab4e79bc4726ecc50c54b9bfb55355eae21473c145d83c2d2"},
|
||||
{file = "lxml-5.3.1.tar.gz", hash = "sha256:106b7b5d2977b339f1e97efe2778e2ab20e99994cbb0ec5e55771ed0795920c8"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
cssselect = ["cssselect (>=0.7)"]
|
||||
html-clean = ["lxml-html-clean"]
|
||||
html-clean = ["lxml_html_clean"]
|
||||
html5 = ["html5lib"]
|
||||
htmlsoup = ["BeautifulSoup4"]
|
||||
source = ["Cython (>=3.0.11)"]
|
||||
source = ["Cython (>=3.0.11,<3.1.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "markdown"
|
||||
@ -7807,8 +7807,9 @@ type = ["pytest-mypy"]
|
||||
ocrmac = ["ocrmac"]
|
||||
rapidocr = ["onnxruntime", "onnxruntime", "rapidocr-onnxruntime"]
|
||||
tesserocr = ["tesserocr"]
|
||||
vlm = ["transformers", "transformers"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.9"
|
||||
content-hash = "1a7435b2a92c3ac353a8a6d2f4a10ba7c909f149c4f29b5b4f44c382aa9559a5"
|
||||
content-hash = "f84601b873a323359d00e8e3f8acf1034bd031ae5f9cf96eb83d91d0704e2be6"
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "docling"
|
||||
version = "2.18.0" # DO NOT EDIT, updated automatically
|
||||
version = "2.20.0" # DO NOT EDIT, updated automatically
|
||||
description = "SDK and CLI for parsing PDF, DOCX, HTML, and more, to a unified document representation for powering downstream workflows such as gen AI applications."
|
||||
authors = ["Christoph Auer <cau@zurich.ibm.com>", "Michele Dolfi <dol@zurich.ibm.com>", "Maxim Lysak <mly@zurich.ibm.com>", "Nikos Livathinos <nli@zurich.ibm.com>", "Ahmed Nassar <ahn@zurich.ibm.com>", "Panos Vagenas <pva@zurich.ibm.com>", "Peter Staar <taa@zurich.ibm.com>"]
|
||||
license = "MIT"
|
||||
@ -59,6 +59,10 @@ onnxruntime = [
|
||||
{ version = ">=1.7.0,<1.20.0", optional = true, markers = "python_version < '3.10'" },
|
||||
{ version = "^1.7.0", optional = true, markers = "python_version >= '3.10'" }
|
||||
]
|
||||
transformers = [
|
||||
{markers = "sys_platform != 'darwin' or platform_machine != 'x86_64'", version = "^4.46.0", optional = true },
|
||||
{markers = "sys_platform == 'darwin' and platform_machine == 'x86_64'", version = "~4.42.0", optional = true }
|
||||
]
|
||||
pillow = "^10.0.0"
|
||||
tqdm = "^4.65.0"
|
||||
|
||||
@ -121,6 +125,7 @@ torchvision = [
|
||||
[tool.poetry.extras]
|
||||
tesserocr = ["tesserocr"]
|
||||
ocrmac = ["ocrmac"]
|
||||
vlm = ["transformers"]
|
||||
rapidocr = ["rapidocr-onnxruntime", "onnxruntime"]
|
||||
|
||||
[tool.poetry.scripts]
|
||||
@ -162,7 +167,8 @@ module = [
|
||||
"deepsearch_glm.*",
|
||||
"lxml.*",
|
||||
"bs4.*",
|
||||
"huggingface_hub.*"
|
||||
"huggingface_hub.*",
|
||||
"transformers.*",
|
||||
]
|
||||
ignore_missing_imports = true
|
||||
|
||||
|
||||
31
tests/data/groundtruth/docling_v2/nested.md.md
Normal file
31
tests/data/groundtruth/docling_v2/nested.md.md
Normal file
@ -0,0 +1,31 @@
|
||||
# Nesting
|
||||
|
||||
A list featuring nesting:
|
||||
|
||||
- abc
|
||||
- abc123
|
||||
- abc1234
|
||||
- abc12345
|
||||
- a.
|
||||
- b.
|
||||
- abcd1234：
|
||||
- abcd12345：
|
||||
- a.
|
||||
- b.
|
||||
- def：
|
||||
- def1234：
|
||||
- def12345。
|
||||
- after one empty line
|
||||
- foo
|
||||
- afer two empty lines
|
||||
- bar
|
||||
|
||||
- changing symbol
|
||||
|
||||
A nested HTML list:
|
||||
|
||||
- First item
|
||||
- Second item with subitems:
|
||||
- Subitem 1
|
||||
- Subitem 2
|
||||
- Last list item
|
||||
66
tests/data/md/nested.md
Normal file
66
tests/data/md/nested.md
Normal file
@ -0,0 +1,66 @@
|
||||
# Nesting
|
||||
|
||||
A list featuring nesting:
|
||||
|
||||
- abc
|
||||
- abc123
|
||||
- abc1234
|
||||
- abc12345
|
||||
- a.
|
||||
- b.
|
||||
- abcd1234：
|
||||
- abcd12345：
|
||||
- a.
|
||||
- b.
|
||||
- def：
|
||||
- def1234：
|
||||
- def12345。
|
||||
|
||||
- after one empty line
|
||||
- foo
|
||||
|
||||
|
||||
- afer two empty lines
|
||||
- bar
|
||||
* changing symbol
|
||||
|
||||
A nested HTML list:
|
||||
|
||||
<ul>
|
||||
<li>First item</li>
|
||||
<li>Second item with subitems:
|
||||
<ul>
|
||||
<li>Subitem 1</li>
|
||||
<li>Subitem 2</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>Last list item</li>
|
||||
</ul>
|
||||
|
||||
<!--
|
||||
Table nesting apparently not yet suported by HTML backend:
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<td>Cell</td>
|
||||
<td>Nested Table
|
||||
<table>
|
||||
<tr>
|
||||
<td>Cell 1</td>
|
||||
<>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Cell 2</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Cell 3</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Cell 4</td>
|
||||
</tr>
|
||||
</table>
|
||||
</td>
|
||||
</tr>
|
||||
<tr><td>additional row</td></tr>
|
||||
</table>
|
||||
-->
|
||||
@ -4,6 +4,8 @@ from docling.backend.md_backend import MarkdownDocumentBackend
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.document import InputDocument
|
||||
|
||||
from .test_data_gen_flag import GEN_TEST_DATA
|
||||
|
||||
|
||||
def test_convert_valid():
|
||||
fmt = InputFormat.MD
|
||||
@ -30,6 +32,10 @@ def test_convert_valid():
|
||||
act_doc = backend.convert()
|
||||
act_data = act_doc.export_to_markdown()
|
||||
|
||||
with open(gt_path, "r", encoding="utf-8") as f:
|
||||
exp_data = f.read().rstrip()
|
||||
assert act_data == exp_data
|
||||
if GEN_TEST_DATA:
|
||||
with open(gt_path, mode="w", encoding="utf-8") as f:
|
||||
f.write(f"{act_data}\n")
|
||||
else:
|
||||
with open(gt_path, encoding="utf-8") as f:
|
||||
exp_data = f.read().rstrip()
|
||||
assert exp_data == act_data
|
||||
|
||||
9
tests/test_data_gen_flag.py
Normal file
9
tests/test_data_gen_flag.py
Normal file
@ -0,0 +1,9 @@
|
||||
import os
|
||||
|
||||
from pydantic import TypeAdapter
|
||||
|
||||
GEN_TEST_DATA = TypeAdapter(bool).validate_python(os.getenv("DOCLING_GEN_TEST_DATA", 0))
|
||||
|
||||
|
||||
def test_gen_test_data_flag():
|
||||
assert not GEN_TEST_DATA
|
||||
Loading…
Reference in New Issue
Block a user