mirror of
https://github.com/DS4SD/docling.git
synced 2025-07-27 12:34:22 +00:00
Merge branch 'main' into rtdl/docx_latex
Signed-off-by: Rafael Teixeira de Lima <Rafael.td.lima@gmail.com>
This commit is contained in:
Rafael Teixeira de Lima
GitHub
commit
2e0a907601
2
.github/actions/setup-poetry/action.yml
vendored
2
.github/actions/setup-poetry/action.yml
vendored
@ -8,7 +8,7 @@ runs:
|
||||
using: 'composite'
|
||||
steps:
|
||||
- name: Install poetry
|
||||
run: pipx install poetry==1.8.3
|
||||
run: pipx install poetry==1.8.5
|
||||
shell: bash
|
||||
- uses: actions/setup-python@v5
|
||||
with:
|
||||
|
||||
2
.github/workflows/checks.yml
vendored
2
.github/workflows/checks.yml
vendored
@ -6,7 +6,7 @@ jobs:
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
matrix:
|
||||
python-version: ['3.9', '3.10', '3.11', '3.12']
|
||||
python-version: ['3.9', '3.10', '3.11', '3.12', '3.13']
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: Install tesseract
|
||||
|
||||
24
CHANGELOG.md
24
CHANGELOG.md
@ -1,3 +1,27 @@
|
||||
## [v2.17.0](https://github.com/DS4SD/docling/releases/tag/v2.17.0) - 2025-01-28
|
||||
|
||||
### Feature
|
||||
|
||||
* **CLI:** Expose code and formula models in the CLI ([#820](https://github.com/DS4SD/docling/issues/820)) ([`6882e6c`](https://github.com/DS4SD/docling/commit/6882e6c38df30e4d4a1b83e01b13900ca7ea001f))
|
||||
* Add platform info to CLI version printout ([#816](https://github.com/DS4SD/docling/issues/816)) ([`95b293a`](https://github.com/DS4SD/docling/commit/95b293a72356f94c7076e3649be970c8a51121a3))
|
||||
* **ocr:** Expose `rec_keys_path` in RapidOcrOptions to support custom dictionaries ([#786](https://github.com/DS4SD/docling/issues/786)) ([`5332755`](https://github.com/DS4SD/docling/commit/53327552e83ced079ae50d8067ba7a8ce80cd9ad))
|
||||
* Introduce automatic language detection in TesseractOcrCliModel ([#800](https://github.com/DS4SD/docling/issues/800)) ([`3be2fb5`](https://github.com/DS4SD/docling/commit/3be2fb581fe5a2ebd5cec9c86bb22eb1dec6fd0f))
|
||||
|
||||
### Fix
|
||||
|
||||
* Fix single newline handling in MD backend ([#824](https://github.com/DS4SD/docling/issues/824)) ([`5aed9f8`](https://github.com/DS4SD/docling/commit/5aed9f8aeba1624ba1a721e2ed3ba4aceaa7a482))
|
||||
* Use file extension if filetype fails with PDF ([#827](https://github.com/DS4SD/docling/issues/827)) ([`adf6353`](https://github.com/DS4SD/docling/commit/adf635348365f82daa64e3f879076a7baf71edc0))
|
||||
* Parse html with omitted body tag ([#818](https://github.com/DS4SD/docling/issues/818)) ([`a112d7a`](https://github.com/DS4SD/docling/commit/a112d7a03512e8a00842a100416426254d6ecfc0))
|
||||
|
||||
### Documentation
|
||||
|
||||
* Document Docling JSON parsing ([#819](https://github.com/DS4SD/docling/issues/819)) ([`6875913`](https://github.com/DS4SD/docling/commit/6875913e34abacb8d71b5d31543adbf7b5bd5e92))
|
||||
* Add SSL verification error mitigation ([#821](https://github.com/DS4SD/docling/issues/821)) ([`5139b48`](https://github.com/DS4SD/docling/commit/5139b48e4e62bb061d956c132958ec2e6d88e40a))
|
||||
* **backend XML:** Do not delete temp file in notebook ([#817](https://github.com/DS4SD/docling/issues/817)) ([`4d41db3`](https://github.com/DS4SD/docling/commit/4d41db3f7abb86c8c65386bf94e7eb0bf22bb82b))
|
||||
* Typo ([#814](https://github.com/DS4SD/docling/issues/814)) ([`8a4ec77`](https://github.com/DS4SD/docling/commit/8a4ec77576b8a9fd60d0047939665d00cf93b4dd))
|
||||
* Added markdown headings to enable TOC in github pages ([#808](https://github.com/DS4SD/docling/issues/808)) ([`b885b2f`](https://github.com/DS4SD/docling/commit/b885b2fa3c2519c399ed4b9a3dd4c2f6f62235d1))
|
||||
* Description of supported formats and backends ([#788](https://github.com/DS4SD/docling/issues/788)) ([`c2ae1cc`](https://github.com/DS4SD/docling/commit/c2ae1cc4cab0f9e693c7ca460fe8afa5b515ee94))
|
||||
|
||||
## [v2.16.0](https://github.com/DS4SD/docling/releases/tag/v2.16.0) - 2025-01-24
|
||||
|
||||
### Feature
|
||||
|
||||
24
README.md
24
README.md
@ -22,23 +22,25 @@
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://pepy.tech/projects/docling)
|
||||
|
||||
Docling parses documents and exports them to the desired format with ease and speed.
|
||||
Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
|
||||
|
||||
## Features
|
||||
|
||||
* 🗂️ Reads popular document formats (PDF, DOCX, PPTX, XLSX, Images, HTML, AsciiDoc & Markdown) and exports to HTML, Markdown and JSON (with embedded and referenced images)
|
||||
* 📑 Advanced PDF document understanding including page layout, reading order & table structures
|
||||
* 🧩 Unified, expressive [DoclingDocument](https://ds4sd.github.io/docling/concepts/docling_document/) representation format
|
||||
* 🤖 Plug-and-play [integrations](https://ds4sd.github.io/docling/integrations/) incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
|
||||
* 🔍 OCR support for scanned PDFs
|
||||
* 🗂️ Parsing of [multiple document formats][supported_formats] incl. PDF, DOCX, XLSX, HTML, images, and more
|
||||
* 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more
|
||||
* 🧬 Unified, expressive [DoclingDocument][docling_document] representation format
|
||||
* ↪️ Various [export formats][supported_formats] and options, including Markdown, HTML, and lossless JSON
|
||||
* 🔒 Local execution capabilities for sensitive data and air-gapped environments
|
||||
* 🤖 Plug-and-play [integrations][integrations] incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
|
||||
* 🔍 Extensive OCR support for scanned PDFs and images
|
||||
* 💻 Simple and convenient CLI
|
||||
|
||||
Explore the [documentation](https://ds4sd.github.io/docling/) to discover plenty examples and unlock the full power of Docling!
|
||||
|
||||
### Coming soon
|
||||
|
||||
* ♾️ Equation & code extraction
|
||||
* 📝 Metadata extraction, including title, authors, references & language
|
||||
* 📝 Inclusion of Visual Language Models ([SmolDocling](https://huggingface.co/blog/smolervlm#smoldocling))
|
||||
* 📝 Chart understanding (Barchart, Piechart, LinePlot, etc)
|
||||
* 📝 Complex chemistry understanding (Molecular structures)
|
||||
|
||||
## Installation
|
||||
|
||||
@ -120,3 +122,7 @@ For individual model usage, please refer to the model licenses found in the orig
|
||||
## IBM ❤️ Open Source AI
|
||||
|
||||
Docling has been brought to you by IBM.
|
||||
|
||||
[supported_formats]: https://ds4sd.github.io/docling/supported_formats/
|
||||
[docling_document]: https://ds4sd.github.io/docling/concepts/docling_document/
|
||||
[integrations]: https://ds4sd.github.io/docling/integrations/
|
||||
|
||||
@ -1,9 +1,9 @@
|
||||
import logging
|
||||
from io import BytesIO
|
||||
from pathlib import Path
|
||||
from typing import Set, Union
|
||||
from typing import Optional, Set, Union
|
||||
|
||||
from bs4 import BeautifulSoup
|
||||
from bs4 import BeautifulSoup, Tag
|
||||
from docling_core.types.doc import (
|
||||
DocItemLabel,
|
||||
DoclingDocument,
|
||||
@ -24,7 +24,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]):
|
||||
super().__init__(in_doc, path_or_stream)
|
||||
_log.debug("About to init HTML backend...")
|
||||
self.soup = None
|
||||
self.soup: Optional[Tag] = None
|
||||
# HTML file:
|
||||
self.path_or_stream = path_or_stream
|
||||
# Initialise the parents for the hierarchy
|
||||
@ -89,7 +89,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
)
|
||||
return doc
|
||||
|
||||
def walk(self, element, doc):
|
||||
def walk(self, element: Tag, doc: DoclingDocument):
|
||||
try:
|
||||
# Iterate over elements in the body of the document
|
||||
for idx, element in enumerate(element.children):
|
||||
@ -106,7 +106,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
|
||||
return doc
|
||||
|
||||
def analyse_element(self, element, idx, doc):
|
||||
def analyse_element(self, element: Tag, idx: int, doc: DoclingDocument):
|
||||
"""
|
||||
if element.name!=None:
|
||||
_log.debug("\t"*self.level, idx, "\t", f"{element.name} ({self.level})")
|
||||
@ -136,7 +136,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
else:
|
||||
self.walk(element, doc)
|
||||
|
||||
def get_direct_text(self, item):
|
||||
def get_direct_text(self, item: Tag):
|
||||
"""Get the direct text of the <li> element (ignoring nested lists)."""
|
||||
text = item.find(string=True, recursive=False)
|
||||
if isinstance(text, str):
|
||||
@ -145,7 +145,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
return ""
|
||||
|
||||
# Function to recursively extract text from all child nodes
|
||||
def extract_text_recursively(self, item):
|
||||
def extract_text_recursively(self, item: Tag):
|
||||
result = []
|
||||
|
||||
if isinstance(item, str):
|
||||
@ -166,7 +166,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
|
||||
return "".join(result) + " "
|
||||
|
||||
def handle_header(self, element, idx, doc):
|
||||
def handle_header(self, element: Tag, idx: int, doc: DoclingDocument):
|
||||
"""Handles header tags (h1, h2, etc.)."""
|
||||
hlevel = int(element.name.replace("h", ""))
|
||||
slevel = hlevel - 1
|
||||
@ -208,7 +208,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
level=hlevel,
|
||||
)
|
||||
|
||||
def handle_code(self, element, idx, doc):
|
||||
def handle_code(self, element: Tag, idx: int, doc: DoclingDocument):
|
||||
"""Handles monospace code snippets (pre)."""
|
||||
if element.text is None:
|
||||
return
|
||||
@ -216,9 +216,9 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
label = DocItemLabel.CODE
|
||||
if len(text) == 0:
|
||||
return
|
||||
doc.add_code(parent=self.parents[self.level], label=label, text=text)
|
||||
doc.add_code(parent=self.parents[self.level], text=text)
|
||||
|
||||
def handle_paragraph(self, element, idx, doc):
|
||||
def handle_paragraph(self, element: Tag, idx: int, doc: DoclingDocument):
|
||||
"""Handles paragraph tags (p)."""
|
||||
if element.text is None:
|
||||
return
|
||||
@ -228,7 +228,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
return
|
||||
doc.add_text(parent=self.parents[self.level], label=label, text=text)
|
||||
|
||||
def handle_list(self, element, idx, doc):
|
||||
def handle_list(self, element: Tag, idx: int, doc: DoclingDocument):
|
||||
"""Handles list tags (ul, ol) and their list items."""
|
||||
|
||||
if element.name == "ul":
|
||||
@ -250,7 +250,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
self.parents[self.level + 1] = None
|
||||
self.level -= 1
|
||||
|
||||
def handle_listitem(self, element, idx, doc):
|
||||
def handle_listitem(self, element: Tag, idx: int, doc: DoclingDocument):
|
||||
"""Handles listitem tags (li)."""
|
||||
nested_lists = element.find(["ul", "ol"])
|
||||
|
||||
@ -304,7 +304,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
else:
|
||||
_log.warn("list-item has no text: ", element)
|
||||
|
||||
def handle_table(self, element, idx, doc):
|
||||
def handle_table(self, element: Tag, idx: int, doc: DoclingDocument):
|
||||
"""Handles table tags."""
|
||||
|
||||
nested_tables = element.find("table")
|
||||
@ -377,7 +377,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
|
||||
doc.add_table(data=data, parent=self.parents[self.level])
|
||||
|
||||
def get_list_text(self, list_element, level=0):
|
||||
def get_list_text(self, list_element: Tag, level=0):
|
||||
"""Recursively extract text from <ul> or <ol> with proper indentation."""
|
||||
result = []
|
||||
bullet_char = "*" # Default bullet character for unordered lists
|
||||
@ -403,7 +403,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
|
||||
return result
|
||||
|
||||
def extract_table_cell_text(self, cell):
|
||||
def extract_table_cell_text(self, cell: Tag):
|
||||
"""Extract text from a table cell, including lists with indents."""
|
||||
contains_lists = cell.find(["ul", "ol"])
|
||||
if contains_lists is None:
|
||||
@ -414,7 +414,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
)
|
||||
return cell.text
|
||||
|
||||
def handle_figure(self, element, idx, doc):
|
||||
def handle_figure(self, element: Tag, idx: int, doc: DoclingDocument):
|
||||
"""Handles image tags (img)."""
|
||||
|
||||
# Extract the image URI from the <img> tag
|
||||
@ -437,6 +437,6 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
|
||||
caption=fig_caption,
|
||||
)
|
||||
|
||||
def handle_image(self, element, idx, doc):
|
||||
def handle_image(self, element: Tag, idx, doc: DoclingDocument):
|
||||
"""Handles image tags (img)."""
|
||||
doc.add_picture(parent=self.parents[self.level], caption=None)
|
||||
|
||||
@ -6,6 +6,7 @@ from pathlib import Path
|
||||
from typing import List, Optional, Set, Union
|
||||
|
||||
import marko
|
||||
import marko.element
|
||||
import marko.ext
|
||||
import marko.ext.gfm
|
||||
import marko.inline
|
||||
@ -65,7 +66,7 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
|
||||
self.in_table = False
|
||||
self.md_table_buffer: list[str] = []
|
||||
self.inline_text_buffer = ""
|
||||
self.inline_texts: list[str] = []
|
||||
|
||||
try:
|
||||
if isinstance(self.path_or_stream, BytesIO):
|
||||
@ -152,26 +153,25 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
def process_inline_text(
|
||||
self, parent_element: Optional[NodeItem], doc: DoclingDocument
|
||||
):
|
||||
# self.inline_text_buffer += str(text_in)
|
||||
txt = self.inline_text_buffer.strip()
|
||||
txt = " ".join(self.inline_texts)
|
||||
if len(txt) > 0:
|
||||
doc.add_text(
|
||||
label=DocItemLabel.PARAGRAPH,
|
||||
parent=parent_element,
|
||||
text=txt,
|
||||
)
|
||||
self.inline_text_buffer = ""
|
||||
self.inline_texts = []
|
||||
|
||||
def iterate_elements(
|
||||
self,
|
||||
element: marko.block.Element,
|
||||
element: marko.element.Element,
|
||||
depth: int,
|
||||
doc: DoclingDocument,
|
||||
parent_element: Optional[NodeItem] = None,
|
||||
):
|
||||
# Iterates over all elements in the AST
|
||||
# Check for different element types and process relevant details
|
||||
if isinstance(element, marko.block.Heading):
|
||||
if isinstance(element, marko.block.Heading) and len(element.children) > 0:
|
||||
self.close_table(doc)
|
||||
self.process_inline_text(parent_element, doc)
|
||||
_log.debug(
|
||||
@ -206,17 +206,22 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
)
|
||||
|
||||
elif isinstance(element, marko.block.List):
|
||||
has_non_empty_list_items = False
|
||||
for child in element.children:
|
||||
if isinstance(child, marko.block.ListItem) and len(child.children) > 0:
|
||||
has_non_empty_list_items = True
|
||||
break
|
||||
|
||||
self.close_table(doc)
|
||||
self.process_inline_text(parent_element, doc)
|
||||
_log.debug(f" - List {'ordered' if element.ordered else 'unordered'}")
|
||||
list_label = GroupLabel.LIST
|
||||
if element.ordered:
|
||||
list_label = GroupLabel.ORDERED_LIST
|
||||
parent_element = doc.add_group(
|
||||
label=list_label, name=f"list", parent=parent_element
|
||||
)
|
||||
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
|
||||
)
|
||||
|
||||
elif isinstance(element, marko.block.ListItem):
|
||||
elif isinstance(element, marko.block.ListItem) and len(element.children) > 0:
|
||||
self.close_table(doc)
|
||||
self.process_inline_text(parent_element, doc)
|
||||
_log.debug(" - List item")
|
||||
@ -246,29 +251,25 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
|
||||
doc.add_picture(parent=parent_element, caption=fig_caption)
|
||||
|
||||
elif isinstance(element, marko.block.Paragraph):
|
||||
elif isinstance(element, marko.block.Paragraph) and len(element.children) > 0:
|
||||
self.process_inline_text(parent_element, doc)
|
||||
|
||||
elif isinstance(element, marko.inline.RawText):
|
||||
_log.debug(f" - Paragraph (raw text): {element.children}")
|
||||
snippet_text = str(element.children).strip()
|
||||
snippet_text = element.children.strip()
|
||||
# Detect start of the table:
|
||||
if "|" in snippet_text:
|
||||
# most likely part of the markdown table
|
||||
self.in_table = True
|
||||
if len(self.md_table_buffer) > 0:
|
||||
self.md_table_buffer[len(self.md_table_buffer) - 1] += str(
|
||||
snippet_text
|
||||
)
|
||||
self.md_table_buffer[len(self.md_table_buffer) - 1] += snippet_text
|
||||
else:
|
||||
self.md_table_buffer.append(snippet_text)
|
||||
else:
|
||||
self.close_table(doc)
|
||||
self.in_table = False
|
||||
# most likely just inline text
|
||||
self.inline_text_buffer += str(
|
||||
element.children
|
||||
) # do not strip an inline text, as it may contain important spaces
|
||||
self.inline_texts.append(str(element.children))
|
||||
|
||||
elif isinstance(element, marko.inline.CodeSpan):
|
||||
self.close_table(doc)
|
||||
@ -277,22 +278,18 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
snippet_text = str(element.children).strip()
|
||||
doc.add_code(parent=parent_element, text=snippet_text)
|
||||
|
||||
elif isinstance(element, marko.block.CodeBlock):
|
||||
elif (
|
||||
isinstance(element, (marko.block.CodeBlock, marko.block.FencedCode))
|
||||
and len(element.children) > 0
|
||||
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)
|
||||
_log.debug(f" - Code Block: {element.children}")
|
||||
snippet_text = str(element.children[0].children).strip() # type: ignore
|
||||
doc.add_code(parent=parent_element, text=snippet_text)
|
||||
|
||||
elif isinstance(element, marko.block.FencedCode):
|
||||
self.close_table(doc)
|
||||
self.process_inline_text(parent_element, doc)
|
||||
_log.debug(f" - Code Block: {element.children}")
|
||||
snippet_text = str(element.children[0].children).strip() # type: ignore
|
||||
doc.add_code(parent=parent_element, text=snippet_text)
|
||||
|
||||
elif isinstance(element, marko.inline.LineBreak):
|
||||
self.process_inline_text(parent_element, doc)
|
||||
if self.in_table:
|
||||
_log.debug("Line break in a table")
|
||||
self.md_table_buffer.append("")
|
||||
@ -313,14 +310,21 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
|
||||
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,
|
||||
)
|
||||
|
||||
# Iterate through the element's children (if any)
|
||||
if not isinstance(element, marko.block.ListItem):
|
||||
if not isinstance(element, marko.block.Heading):
|
||||
if not isinstance(element, marko.block.FencedCode):
|
||||
# if not isinstance(element, marko.block.Paragraph):
|
||||
if hasattr(element, "children"):
|
||||
for child in element.children:
|
||||
self.iterate_elements(child, depth + 1, doc, parent_element)
|
||||
if hasattr(element, "children") and not isinstance(
|
||||
element, processed_block_types
|
||||
):
|
||||
for child in element.children:
|
||||
self.iterate_elements(child, depth + 1, doc, parent_element)
|
||||
|
||||
def is_valid(self) -> bool:
|
||||
return self.valid
|
||||
|
||||
@ -271,13 +271,12 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
|
||||
return
|
||||
|
||||
def handle_pictures(self, shape, parent_slide, slide_ind, doc):
|
||||
# Get the image bytes
|
||||
image = shape.image
|
||||
image_bytes = image.blob
|
||||
im_dpi, _ = image.dpi
|
||||
|
||||
# Open it with PIL
|
||||
try:
|
||||
# Get the image bytes
|
||||
image = shape.image
|
||||
image_bytes = image.blob
|
||||
im_dpi, _ = image.dpi
|
||||
pil_image = Image.open(BytesIO(image_bytes))
|
||||
|
||||
# shape has picture
|
||||
|
||||
@ -261,7 +261,11 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
|
||||
numid = None
|
||||
|
||||
# Handle lists
|
||||
if numid is not None and ilevel is not None:
|
||||
if (
|
||||
numid is not None
|
||||
and ilevel is not None
|
||||
and p_style_id not in ["Title", "Heading"]
|
||||
):
|
||||
self.add_listitem(
|
||||
element,
|
||||
docx_obj,
|
||||
@ -275,12 +279,21 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
|
||||
)
|
||||
self.update_history(p_style_id, p_level, numid, ilevel)
|
||||
return
|
||||
elif numid is None and self.prev_numid() is not None: # Close list
|
||||
for key, val in self.parents.items():
|
||||
if key >= self.level_at_new_list:
|
||||
elif (
|
||||
numid is None
|
||||
and self.prev_numid() is not None
|
||||
and p_style_id not in ["Title", "Heading"]
|
||||
): # Close list
|
||||
if self.level_at_new_list:
|
||||
for key in range(len(self.parents)):
|
||||
if key >= self.level_at_new_list:
|
||||
self.parents[key] = None
|
||||
self.level = self.level_at_new_list - 1
|
||||
self.level_at_new_list = None
|
||||
else:
|
||||
for key in range(len(self.parents)):
|
||||
self.parents[key] = None
|
||||
self.level = self.level_at_new_list - 1
|
||||
self.level_at_new_list = None
|
||||
self.level = 0
|
||||
|
||||
if p_style_id in ["Title"]:
|
||||
for key, val in self.parents.items():
|
||||
@ -559,11 +572,11 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
|
||||
image_data = image_part.blob # Get the binary image data
|
||||
return image_data
|
||||
|
||||
image_data = get_docx_image(element, drawing_blip)
|
||||
image_bytes = BytesIO(image_data)
|
||||
level = self.get_level()
|
||||
# Open the BytesIO object with PIL to create an Image
|
||||
try:
|
||||
image_data = get_docx_image(element, drawing_blip)
|
||||
image_bytes = BytesIO(image_data)
|
||||
pil_image = Image.open(image_bytes)
|
||||
doc.add_picture(
|
||||
parent=self.parents[level - 1],
|
||||
|
||||
@ -352,6 +352,8 @@ class _DocumentConversionInput(BaseModel):
|
||||
mime = FormatToMimeType[InputFormat.MD][0]
|
||||
elif ext in FormatToExtensions[InputFormat.JSON_DOCLING]:
|
||||
mime = FormatToMimeType[InputFormat.JSON_DOCLING][0]
|
||||
elif ext in FormatToExtensions[InputFormat.PDF]:
|
||||
mime = FormatToMimeType[InputFormat.PDF][0]
|
||||
return mime
|
||||
|
||||
@staticmethod
|
||||
|
||||
29
docs/examples/inspect_picture_content.py
Normal file
29
docs/examples/inspect_picture_content.py
Normal file
@ -0,0 +1,29 @@
|
||||
from docling_core.types.doc import TextItem
|
||||
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.pipeline_options import PdfPipelineOptions
|
||||
from docling.document_converter import DocumentConverter, PdfFormatOption
|
||||
|
||||
source = "tests/data/amt_handbook_sample.pdf"
|
||||
|
||||
pipeline_options = PdfPipelineOptions()
|
||||
pipeline_options.images_scale = 2
|
||||
pipeline_options.generate_page_images = True
|
||||
|
||||
doc_converter = DocumentConverter(
|
||||
format_options={InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)}
|
||||
)
|
||||
|
||||
result = doc_converter.convert(source)
|
||||
|
||||
doc = result.document
|
||||
|
||||
for picture in doc.pictures:
|
||||
# picture.get_image(doc).show() # display the picture
|
||||
print(picture.caption_text(doc), " contains these elements:")
|
||||
|
||||
for item, level in doc.iterate_items(root=picture, traverse_pictures=True):
|
||||
if isinstance(item, TextItem):
|
||||
print(item.text)
|
||||
|
||||
print("\n")
|
||||
31
docs/faq.md
31
docs/faq.md
@ -7,28 +7,7 @@ This is a collection of FAQ collected from the user questions on <https://github
|
||||
|
||||
### Is Python 3.13 supported?
|
||||
|
||||
Full support for Python 3.13 is currently waiting for [pytorch](https://github.com/pytorch/pytorch).
|
||||
|
||||
At the moment, no release has full support, but nightly builds are available. Docling was tested on Python 3.13 with the following steps:
|
||||
|
||||
```sh
|
||||
# Create a python 3.13 virtualenv
|
||||
python3.13 -m venv venv
|
||||
source ./venv/bin/activate
|
||||
|
||||
# Install torch nightly builds, see https://pytorch.org/
|
||||
pip3 install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu
|
||||
|
||||
# Install docling
|
||||
pip3 install docling
|
||||
|
||||
# Run docling
|
||||
docling --no-ocr https://arxiv.org/pdf/2408.09869
|
||||
```
|
||||
|
||||
_Note: we are disabling OCR since easyocr and the nightly torch builds have some conflicts._
|
||||
|
||||
Source: Issue [#136](https://github.com/DS4SD/docling/issues/136)
|
||||
Python 3.13 is supported from Docling 2.18.0.
|
||||
|
||||
|
||||
??? question "Install conflicts with numpy (python 3.13)"
|
||||
@ -151,3 +130,11 @@ This is a collection of FAQ collected from the user questions on <https://github
|
||||
pipeline_options = PdfPipelineOptions()
|
||||
pipeline_options.ocr_options.lang = ["fr", "de", "es", "en"] # example of languages for EasyOCR
|
||||
```
|
||||
|
||||
|
||||
??? Some images are missing from MS Word and Powerpoint"
|
||||
|
||||
### Some images are missing from MS Word and Powerpoint
|
||||
|
||||
The image processing library used by Docling is able to handle embedded WMF images only on Windows platform.
|
||||
If you are on other operaring systems, these images will be ignored.
|
||||
|
||||
@ -14,21 +14,25 @@
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://pepy.tech/projects/docling)
|
||||
|
||||
Docling parses documents and exports them to the desired format with ease and speed.
|
||||
Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
|
||||
|
||||
## Features
|
||||
|
||||
* 🗂️ Reads popular document formats (PDF, DOCX, PPTX, XLSX, Images, HTML, AsciiDoc & Markdown) and exports to HTML, Markdown and JSON (with embedded and referenced images)
|
||||
* 📑 Advanced PDF document understanding incl. page layout, reading order & table structures
|
||||
* 🧩 Unified, expressive [DoclingDocument](./concepts/docling_document.md) representation format
|
||||
* 🤖 Plug-and-play [integrations](https://ds4sd.github.io/docling/integrations/) incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
|
||||
* 🔍 OCR support for scanned PDFs
|
||||
* 🗂️ Parsing of [multiple document formats][supported_formats] incl. PDF, DOCX, XLSX, HTML, images, and more
|
||||
* 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more
|
||||
* 🧬 Unified, expressive [DoclingDocument][docling_document] representation format
|
||||
* ↪️ Various [export formats][supported_formats] and options, including Markdown, HTML, and lossless JSON
|
||||
* 🔒 Local execution capabilities for sensitive data and air-gapped environments
|
||||
* 🤖 Plug-and-play [integrations][integrations] incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
|
||||
* 🔍 Extensive OCR support for scanned PDFs and images
|
||||
* 💻 Simple and convenient CLI
|
||||
|
||||
### Coming soon
|
||||
|
||||
* ♾️ Equation & code extraction
|
||||
* 📝 Metadata extraction, including title, authors, references & language
|
||||
* 📝 Inclusion of Visual Language Models ([SmolDocling](https://huggingface.co/blog/smolervlm#smoldocling))
|
||||
* 📝 Chart understanding (Barchart, Piechart, LinePlot, etc)
|
||||
* 📝 Complex chemistry understanding (Molecular structures)
|
||||
|
||||
## Get started
|
||||
|
||||
@ -42,3 +46,7 @@ Docling parses documents and exports them to the desired format with ease and sp
|
||||
## IBM ❤️ Open Source AI
|
||||
|
||||
Docling has been brought to you by IBM.
|
||||
|
||||
[supported_formats]: ./supported_formats.md
|
||||
[docling_document]: ./concepts/docling_document.md
|
||||
[integrations]: ./integrations/index.md
|
||||
|
||||
34
docs/supported_formats.md
Normal file
34
docs/supported_formats.md
Normal file
@ -0,0 +1,34 @@
|
||||
Docling can parse various documents formats into a unified representation (Docling
|
||||
Document), which it can export to different formats too — check out
|
||||
[Architecture](./concepts/architecture.md) for more details.
|
||||
|
||||
Below you can find a listing of all supported input and output formats.
|
||||
|
||||
## Supported input formats
|
||||
|
||||
| Format | Description |
|
||||
|--------|-------------|
|
||||
| PDF | |
|
||||
| DOCX, XLSX, PPTX | Default formats in MS Office 2007+, based on Office Open XML |
|
||||
| Markdown | |
|
||||
| AsciiDoc | |
|
||||
| HTML, XHTML | |
|
||||
| PNG, JPEG, TIFF, BMP | Image formats |
|
||||
|
||||
Schema-specific support:
|
||||
|
||||
| Format | Description |
|
||||
|--------|-------------|
|
||||
| USPTO XML | XML format followed by [USPTO](https://www.uspto.gov/patents) patents |
|
||||
| PMC XML | XML format followed by [PubMed Central®](https://pmc.ncbi.nlm.nih.gov/) articles |
|
||||
| Docling JSON | JSON-serialized [Docling Document](./concepts/docling_document.md) |
|
||||
|
||||
## Supported output formats
|
||||
|
||||
| Format | Description |
|
||||
|--------|-------------|
|
||||
| HTML | Both image embedding and referencing are supported |
|
||||
| Markdown | |
|
||||
| JSON | Lossless serialization of Docling Document |
|
||||
| Text | Plain text, i.e. without Markdown markers |
|
||||
| Doctags | |
|
||||
@ -24,20 +24,6 @@ docling https://arxiv.org/pdf/2206.01062
|
||||
|
||||
To see all available options (export formats etc.) run `docling --help`. More details in the [CLI reference page](./reference/cli.md).
|
||||
|
||||
### Supported formats
|
||||
|
||||
The document conversion in Docling supports several popular formats, including:
|
||||
|
||||
- **PDF** (Portable Document Format): the format developed by Adobe to present documents compatible across application software, hardware, and operating systems.
|
||||
- **.docx**, **.xlsx**, **.pptx** (Word, Excel, and PowerPoint): the Open XML formats suppored by Microsof Office.
|
||||
- **Markdown**: a lightweight markup language to add formatting elements to plain text documents.
|
||||
- **AsciiDoc**: a plain text markup language for writing technical content.
|
||||
- **HTML** (Hypertext Markup Language): the standard markup language for creating web pages.
|
||||
- **XHTML** (Extensible Hypertext Markup Language): the XML-based version of HTML.
|
||||
- **XML** (Extensible Markup Language): a markup format for storing and transmitting data. Due to its flexibility, Docling requires custom implementations to identify the
|
||||
semantics of the data. Currently, Docling supports the parsing of [USPTO](https://www.uspto.gov/patents) patents and [PubMed Central® (PMC)](https://pmc.ncbi.nlm.nih.gov/) articles.
|
||||
|
||||
|
||||
### Advanced options
|
||||
|
||||
#### Adjust pipeline features
|
||||
@ -142,7 +128,14 @@ You can limit the CPU threads used by Docling by setting the environment variabl
|
||||
|
||||
#### Use specific backend converters
|
||||
|
||||
By default, Docling will try to identify the document format to apply the appropriate conversion backend (see the list of [supported formats](#supported-formats)).
|
||||
!!! note
|
||||
|
||||
This section discusses directly invoking a [backend](./concepts/architecture.md),
|
||||
i.e. using a low-level API. This should only be done when necessary. For most cases,
|
||||
using a `DocumentConverter` (high-level API) as discussed in the sections above
|
||||
should suffice — and is the recommended way.
|
||||
|
||||
By default, Docling will try to identify the document format to apply the appropriate conversion backend (see the list of [supported formats](./supported_formats.md)).
|
||||
You can restrict the `DocumentConverter` to a set of allowed document formats, as shown in the [Multi-format conversion](./examples/run_with_formats.py) example.
|
||||
Alternatively, you can also use the specific backend that matches your document content. For instance, you can use `HTMLDocumentBackend` for HTML pages:
|
||||
|
||||
@ -162,8 +155,8 @@ in_doc = InputDocument(
|
||||
filename="duck.html",
|
||||
)
|
||||
backend = HTMLDocumentBackend(in_doc=in_doc, path_or_stream=BytesIO(text))
|
||||
result = backend.convert()
|
||||
print(result.export_to_markdown())
|
||||
dl_doc = backend.convert()
|
||||
print(dl_doc.export_to_markdown())
|
||||
```
|
||||
|
||||
## Chunking
|
||||
|
||||
@ -56,6 +56,7 @@ nav:
|
||||
- "Docling": index.md
|
||||
- Installation: installation.md
|
||||
- Usage: usage.md
|
||||
- Supported formats: supported_formats.md
|
||||
- FAQ: faq.md
|
||||
- Docling v2: v2.md
|
||||
- Concepts:
|
||||
@ -77,7 +78,7 @@ nav:
|
||||
- "Force full page OCR": examples/full_page_ocr.py
|
||||
- "Automatic OCR language detection with tesseract": examples/tesseract_lang_detection.py
|
||||
- "Accelerator options": examples/run_with_accelerator.py
|
||||
- "Simple translation": examples/translate.py
|
||||
- "Simple translation": examples/translate.py
|
||||
- examples/backend_xml_rag.ipynb
|
||||
- ✂️ Chunking:
|
||||
- examples/hybrid_chunking.ipynb
|
||||
|
||||
470
poetry.lock
generated
470
poetry.lock
generated
@ -182,8 +182,8 @@ files = [
|
||||
lazy-object-proxy = ">=1.4.0"
|
||||
typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
|
||||
wrapt = [
|
||||
{version = ">=1.11,<2", markers = "python_version < \"3.11\""},
|
||||
{version = ">=1.14,<2", markers = "python_version >= \"3.11\""},
|
||||
{version = ">=1.11,<2", markers = "python_version < \"3.11\""},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -861,13 +861,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "docling-core"
|
||||
version = "2.15.1"
|
||||
version = "2.16.1"
|
||||
description = "A python library to define and validate data types in Docling."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "docling_core-2.15.1-py3-none-any.whl", hash = "sha256:33152604e1f14d5caccbef099c73163c3f211d0b4d92403d262c308633cc0451"},
|
||||
{file = "docling_core-2.15.1.tar.gz", hash = "sha256:588d941b5bfc393a79e779ab64819c60763e7f182ec5221ee37da4be91dd802f"},
|
||||
{file = "docling_core-2.16.1-py3-none-any.whl", hash = "sha256:d26af2f49e9f1f65ae5dfca972e206860339c1f91adfe427fa67d1cf95cce241"},
|
||||
{file = "docling_core-2.16.1.tar.gz", hash = "sha256:676f51fa5797c91a86ccbc1fdaa020effcde4cc86aa9b094a0d5d775636871ba"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -1488,13 +1488,13 @@ zstd = ["zstandard (>=0.18.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "huggingface-hub"
|
||||
version = "0.27.1"
|
||||
version = "0.28.0"
|
||||
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
|
||||
optional = false
|
||||
python-versions = ">=3.8.0"
|
||||
files = [
|
||||
{file = "huggingface_hub-0.27.1-py3-none-any.whl", hash = "sha256:1c5155ca7d60b60c2e2fc38cbb3ffb7f7c3adf48f824015b219af9061771daec"},
|
||||
{file = "huggingface_hub-0.27.1.tar.gz", hash = "sha256:c004463ca870283909d715d20f066ebd6968c2207dae9393fdffb3c1d4d8f98b"},
|
||||
{file = "huggingface_hub-0.28.0-py3-none-any.whl", hash = "sha256:71cff4e500efe68061d94b7f6d3114e183715088be7a90bf4dd84af83b5f5cdb"},
|
||||
{file = "huggingface_hub-0.28.0.tar.gz", hash = "sha256:c2b18c02a47d4384763caddb4d0ab2a8fc6c16e0800d6de4d55d0a896244aba3"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -1507,13 +1507,13 @@ tqdm = ">=4.42.1"
|
||||
typing-extensions = ">=3.7.4.3"
|
||||
|
||||
[package.extras]
|
||||
all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
||||
all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
||||
cli = ["InquirerPy (==0.3.4)"]
|
||||
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
||||
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
||||
fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
|
||||
hf-transfer = ["hf-transfer (>=0.1.4)"]
|
||||
inference = ["aiohttp"]
|
||||
quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.5.0)"]
|
||||
quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.9.0)"]
|
||||
tensorflow = ["graphviz", "pydot", "tensorflow"]
|
||||
tensorflow-testing = ["keras (<3.0)", "tensorflow"]
|
||||
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
|
||||
@ -2598,13 +2598,13 @@ tqdm = "*"
|
||||
|
||||
[[package]]
|
||||
name = "mistune"
|
||||
version = "3.1.0"
|
||||
version = "3.1.1"
|
||||
description = "A sane and fast Markdown parser with useful plugins and renderers"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mistune-3.1.0-py3-none-any.whl", hash = "sha256:b05198cf6d671b3deba6c87ec6cf0d4eb7b72c524636eddb6dbf13823b52cee1"},
|
||||
{file = "mistune-3.1.0.tar.gz", hash = "sha256:dbcac2f78292b9dc066cd03b7a3a26b62d85f8159f2ea5fd28e55df79908d667"},
|
||||
{file = "mistune-3.1.1-py3-none-any.whl", hash = "sha256:02106ac2aa4f66e769debbfa028509a275069dcffce0dfa578edd7b991ee700a"},
|
||||
{file = "mistune-3.1.1.tar.gz", hash = "sha256:e0740d635f515119f7d1feb6f9b192ee60f0cc649f80a8f944f905706a21654c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -2817,8 +2817,8 @@ files = [
|
||||
|
||||
[package.dependencies]
|
||||
multiprocess = [
|
||||
{version = "*", optional = true, markers = "python_version < \"3.11\" and extra == \"dill\""},
|
||||
{version = ">=0.70.15", optional = true, markers = "python_version >= \"3.11\" and extra == \"dill\""},
|
||||
{version = "*", optional = true, markers = "python_version < \"3.11\" and extra == \"dill\""},
|
||||
]
|
||||
pygments = ">=2.0"
|
||||
pywin32 = {version = ">=301", markers = "platform_system == \"Windows\""}
|
||||
@ -3069,13 +3069,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
|
||||
|
||||
[[package]]
|
||||
name = "nbconvert"
|
||||
version = "7.16.5"
|
||||
version = "7.16.6"
|
||||
description = "Converting Jupyter Notebooks (.ipynb files) to other formats. Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script. nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "nbconvert-7.16.5-py3-none-any.whl", hash = "sha256:e12eac052d6fd03040af4166c563d76e7aeead2e9aadf5356db552a1784bd547"},
|
||||
{file = "nbconvert-7.16.5.tar.gz", hash = "sha256:c83467bb5777fdfaac5ebbb8e864f300b277f68692ecc04d6dab72f2d8442344"},
|
||||
{file = "nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b"},
|
||||
{file = "nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -3640,6 +3640,18 @@ files = [
|
||||
[package.dependencies]
|
||||
nvidia-nvjitlink-cu12 = "*"
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cusparselt-cu12"
|
||||
version = "0.6.2"
|
||||
description = "NVIDIA cuSPARSELt"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:067a7f6d03ea0d4841c85f0c6f1991c5dda98211f6302cb83a4ab234ee95bef8"},
|
||||
{file = "nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:df2c24502fd76ebafe7457dbc4716b2fec071aabaed4fb7691a201cde03704d9"},
|
||||
{file = "nvidia_cusparselt_cu12-0.6.2-py3-none-win_amd64.whl", hash = "sha256:0057c91d230703924c0422feabe4ce768841f9b4b44d28586b6f6d2eb86fbe70"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-nccl-cu12"
|
||||
version = "2.19.3"
|
||||
@ -3821,12 +3833,12 @@ files = [
|
||||
|
||||
[package.dependencies]
|
||||
numpy = [
|
||||
{version = ">=1.21.0", markers = "python_version == \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\""},
|
||||
{version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
{version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
|
||||
{version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
|
||||
{version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
|
||||
{version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
{version = ">=1.21.0", markers = "python_version == \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\""},
|
||||
{version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -3847,12 +3859,12 @@ files = [
|
||||
|
||||
[package.dependencies]
|
||||
numpy = [
|
||||
{version = ">=1.21.0", markers = "python_version == \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\""},
|
||||
{version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
{version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
|
||||
{version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
|
||||
{version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
|
||||
{version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
{version = ">=1.21.0", markers = "python_version == \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\""},
|
||||
{version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -4036,9 +4048,9 @@ files = [
|
||||
|
||||
[package.dependencies]
|
||||
numpy = [
|
||||
{version = ">=1.22.4", markers = "python_version < \"3.11\""},
|
||||
{version = ">=1.23.2", markers = "python_version == \"3.11\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
{version = ">=1.23.2", markers = "python_version == \"3.11\""},
|
||||
{version = ">=1.22.4", markers = "python_version < \"3.11\""},
|
||||
]
|
||||
python-dateutil = ">=2.8.2"
|
||||
pytz = ">=2020.1"
|
||||
@ -4812,8 +4824,8 @@ files = [
|
||||
astroid = ">=2.15.8,<=2.17.0-dev0"
|
||||
colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
|
||||
dill = [
|
||||
{version = ">=0.2", markers = "python_version < \"3.11\""},
|
||||
{version = ">=0.3.6", markers = "python_version >= \"3.11\""},
|
||||
{version = ">=0.2", markers = "python_version < \"3.11\""},
|
||||
]
|
||||
isort = ">=4.2.5,<6"
|
||||
mccabe = ">=0.6,<0.8"
|
||||
@ -4828,13 +4840,13 @@ testutils = ["gitpython (>3)"]
|
||||
|
||||
[[package]]
|
||||
name = "pymdown-extensions"
|
||||
version = "10.14.1"
|
||||
version = "10.14.2"
|
||||
description = "Extension pack for Python Markdown."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pymdown_extensions-10.14.1-py3-none-any.whl", hash = "sha256:637951cbfbe9874ba28134fb3ce4b8bcadd6aca89ac4998ec29dcbafd554ae08"},
|
||||
{file = "pymdown_extensions-10.14.1.tar.gz", hash = "sha256:b65801996a0cd4f42a3110810c306c45b7313c09b0610a6f773730f2a9e3c96b"},
|
||||
{file = "pymdown_extensions-10.14.2-py3-none-any.whl", hash = "sha256:f45bc5892410e54fd738ab8ccd736098b7ff0cb27fdb4bf24d0a0c6584bc90e1"},
|
||||
{file = "pymdown_extensions-10.14.2.tar.gz", hash = "sha256:7a77b8116dc04193f2c01143760a43387bd9dc4aa05efacb7d838885a7791253"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -5429,120 +5441,120 @@ pyyaml = "*"
|
||||
|
||||
[[package]]
|
||||
name = "pyzmq"
|
||||
version = "26.2.0"
|
||||
version = "26.2.1"
|
||||
description = "Python bindings for 0MQ"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "pyzmq-26.2.0-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:ddf33d97d2f52d89f6e6e7ae66ee35a4d9ca6f36eda89c24591b0c40205a3629"},
|
||||
{file = "pyzmq-26.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:dacd995031a01d16eec825bf30802fceb2c3791ef24bcce48fa98ce40918c27b"},
|
||||
{file = "pyzmq-26.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:89289a5ee32ef6c439086184529ae060c741334b8970a6855ec0b6ad3ff28764"},
|
||||
{file = "pyzmq-26.2.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5506f06d7dc6ecf1efacb4a013b1f05071bb24b76350832c96449f4a2d95091c"},
|
||||
{file = "pyzmq-26.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8ea039387c10202ce304af74def5021e9adc6297067f3441d348d2b633e8166a"},
|
||||
{file = "pyzmq-26.2.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:a2224fa4a4c2ee872886ed00a571f5e967c85e078e8e8c2530a2fb01b3309b88"},
|
||||
{file = "pyzmq-26.2.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:28ad5233e9c3b52d76196c696e362508959741e1a005fb8fa03b51aea156088f"},
|
||||
{file = "pyzmq-26.2.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:1c17211bc037c7d88e85ed8b7d8f7e52db6dc8eca5590d162717c654550f7282"},
|
||||
{file = "pyzmq-26.2.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:b8f86dd868d41bea9a5f873ee13bf5551c94cf6bc51baebc6f85075971fe6eea"},
|
||||
{file = "pyzmq-26.2.0-cp310-cp310-win32.whl", hash = "sha256:46a446c212e58456b23af260f3d9fb785054f3e3653dbf7279d8f2b5546b21c2"},
|
||||
{file = "pyzmq-26.2.0-cp310-cp310-win_amd64.whl", hash = "sha256:49d34ab71db5a9c292a7644ce74190b1dd5a3475612eefb1f8be1d6961441971"},
|
||||
{file = "pyzmq-26.2.0-cp310-cp310-win_arm64.whl", hash = "sha256:bfa832bfa540e5b5c27dcf5de5d82ebc431b82c453a43d141afb1e5d2de025fa"},
|
||||
{file = "pyzmq-26.2.0-cp311-cp311-macosx_10_15_universal2.whl", hash = "sha256:8f7e66c7113c684c2b3f1c83cdd3376103ee0ce4c49ff80a648643e57fb22218"},
|
||||
{file = "pyzmq-26.2.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3a495b30fc91db2db25120df5847d9833af237546fd59170701acd816ccc01c4"},
|
||||
{file = "pyzmq-26.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:77eb0968da535cba0470a5165468b2cac7772cfb569977cff92e240f57e31bef"},
|
||||
{file = "pyzmq-26.2.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6ace4f71f1900a548f48407fc9be59c6ba9d9aaf658c2eea6cf2779e72f9f317"},
|
||||
{file = "pyzmq-26.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:92a78853d7280bffb93df0a4a6a2498cba10ee793cc8076ef797ef2f74d107cf"},
|
||||
{file = "pyzmq-26.2.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:689c5d781014956a4a6de61d74ba97b23547e431e9e7d64f27d4922ba96e9d6e"},
|
||||
{file = "pyzmq-26.2.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:0aca98bc423eb7d153214b2df397c6421ba6373d3397b26c057af3c904452e37"},
|
||||
{file = "pyzmq-26.2.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:1f3496d76b89d9429a656293744ceca4d2ac2a10ae59b84c1da9b5165f429ad3"},
|
||||
{file = "pyzmq-26.2.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:5c2b3bfd4b9689919db068ac6c9911f3fcb231c39f7dd30e3138be94896d18e6"},
|
||||
{file = "pyzmq-26.2.0-cp311-cp311-win32.whl", hash = "sha256:eac5174677da084abf378739dbf4ad245661635f1600edd1221f150b165343f4"},
|
||||
{file = "pyzmq-26.2.0-cp311-cp311-win_amd64.whl", hash = "sha256:5a509df7d0a83a4b178d0f937ef14286659225ef4e8812e05580776c70e155d5"},
|
||||
{file = "pyzmq-26.2.0-cp311-cp311-win_arm64.whl", hash = "sha256:c0e6091b157d48cbe37bd67233318dbb53e1e6327d6fc3bb284afd585d141003"},
|
||||
{file = "pyzmq-26.2.0-cp312-cp312-macosx_10_15_universal2.whl", hash = "sha256:ded0fc7d90fe93ae0b18059930086c51e640cdd3baebdc783a695c77f123dcd9"},
|
||||
{file = "pyzmq-26.2.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:17bf5a931c7f6618023cdacc7081f3f266aecb68ca692adac015c383a134ca52"},
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|
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{file = "torch-2.6.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:c4f103a49830ce4c7561ef4434cc7926e5a5fe4e5eb100c19ab36ea1e2b634ab"},
|
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{file = "torch-2.6.0-cp310-cp310-win_amd64.whl", hash = "sha256:56eeaf2ecac90da5d9e35f7f35eb286da82673ec3c582e310a8d1631a1c02341"},
|
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{file = "torch-2.6.0-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:09e06f9949e1a0518c5b09fe95295bc9661f219d9ecb6f9893e5123e10696628"},
|
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{file = "torch-2.6.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:7979834102cd5b7a43cc64e87f2f3b14bd0e1458f06e9f88ffa386d07c7446e1"},
|
||||
{file = "torch-2.6.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:ccbd0320411fe1a3b3fec7b4d3185aa7d0c52adac94480ab024b5c8f74a0bf1d"},
|
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{file = "torch-2.6.0-cp311-cp311-win_amd64.whl", hash = "sha256:46763dcb051180ce1ed23d1891d9b1598e07d051ce4c9d14307029809c4d64f7"},
|
||||
{file = "torch-2.6.0-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:94fc63b3b4bedd327af588696559f68c264440e2503cc9e6954019473d74ae21"},
|
||||
{file = "torch-2.6.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:2bb8987f3bb1ef2675897034402373ddfc8f5ef0e156e2d8cfc47cacafdda4a9"},
|
||||
{file = "torch-2.6.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:b789069020c5588c70d5c2158ac0aa23fd24a028f34a8b4fcb8fcb4d7efcf5fb"},
|
||||
{file = "torch-2.6.0-cp312-cp312-win_amd64.whl", hash = "sha256:7e1448426d0ba3620408218b50aa6ada88aeae34f7a239ba5431f6c8774b1239"},
|
||||
{file = "torch-2.6.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:9a610afe216a85a8b9bc9f8365ed561535c93e804c2a317ef7fabcc5deda0989"},
|
||||
{file = "torch-2.6.0-cp313-cp313-manylinux1_x86_64.whl", hash = "sha256:4874a73507a300a5d089ceaff616a569e7bb7c613c56f37f63ec3ffac65259cf"},
|
||||
{file = "torch-2.6.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:a0d5e1b9874c1a6c25556840ab8920569a7a4137afa8a63a32cee0bc7d89bd4b"},
|
||||
{file = "torch-2.6.0-cp313-cp313-win_amd64.whl", hash = "sha256:510c73251bee9ba02ae1cb6c9d4ee0907b3ce6020e62784e2d7598e0cfa4d6cc"},
|
||||
{file = "torch-2.6.0-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:ff96f4038f8af9f7ec4231710ed4549da1bdebad95923953a25045dcf6fd87e2"},
|
||||
{file = "torch-2.6.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:9ea955317cfcd3852b1402b62af258ce735c2edeee42ca9419b6bc889e5ae053"},
|
||||
{file = "torch-2.6.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:bb2c6c3e65049f081940f5ab15c9136c7de40d3f01192541c920a07c7c585b7e"},
|
||||
{file = "torch-2.6.0-cp39-cp39-win_amd64.whl", hash = "sha256:683410f97984103148e31b38a8631acf31c3034c020c0f4d26171e7626d8317a"},
|
||||
{file = "torch-2.6.0-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:265f70de5fd45b864d924b64be1797f86e76c8e48a02c2a3a6fc7ec247d2226c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -6770,17 +6842,18 @@ nvidia-cufft-cu12 = {version = "11.2.1.3", markers = "platform_system == \"Linux
|
||||
nvidia-curand-cu12 = {version = "10.3.5.147", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
|
||||
nvidia-cusolver-cu12 = {version = "11.6.1.9", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
|
||||
nvidia-cusparse-cu12 = {version = "12.3.1.170", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
|
||||
nvidia-cusparselt-cu12 = {version = "0.6.2", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
|
||||
nvidia-nccl-cu12 = {version = "2.21.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
|
||||
nvidia-nvjitlink-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
|
||||
nvidia-nvtx-cu12 = {version = "12.4.127", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
|
||||
setuptools = {version = "*", markers = "python_version >= \"3.12\""}
|
||||
sympy = {version = "1.13.1", markers = "python_version >= \"3.9\""}
|
||||
triton = {version = "3.1.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""}
|
||||
typing-extensions = ">=4.8.0"
|
||||
triton = {version = "3.2.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
|
||||
typing-extensions = ">=4.10.0"
|
||||
|
||||
[package.extras]
|
||||
opt-einsum = ["opt-einsum (>=3.3)"]
|
||||
optree = ["optree (>=0.12.0)"]
|
||||
optree = ["optree (>=0.13.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "torchvision"
|
||||
@ -6826,33 +6899,37 @@ scipy = ["scipy"]
|
||||
|
||||
[[package]]
|
||||
name = "torchvision"
|
||||
version = "0.20.1"
|
||||
version = "0.21.0"
|
||||
description = "image and video datasets and models for torch deep learning"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "torchvision-0.20.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4878fefb96ef293d06c27210918adc83c399d9faaf34cda5a63e129f772328f1"},
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{file = "torchvision-0.20.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:8ffbdf8bf5b30eade22d459f5a313329eeadb20dc75efa142987b53c007098c3"},
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{file = "torchvision-0.20.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:75f8a4d51a593c4bab6c9bf7d75bdd88691b00a53b07656678bc55a3a753dd73"},
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{file = "torchvision-0.20.1-cp310-cp310-win_amd64.whl", hash = "sha256:22c2fa44e20eb404b85e42b22b453863a14b0927d25e550fd4f84eea97fa5b39"},
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{file = "torchvision-0.20.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:344b339e15e6bbb59ee0700772616d0afefd209920c762b1604368d8c3458322"},
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{file = "torchvision-0.20.1-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:86f6523dee420000fe14c3527f6c8e0175139fda7d995b187f54a0b0ebec7eb6"},
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{file = "torchvision-0.20.1-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:a40d766345927639da322c693934e5f91b1ba2218846c7104b868dea2314ce8e"},
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{file = "torchvision-0.20.1-cp311-cp311-win_amd64.whl", hash = "sha256:5b501d5c04b034d2ecda96a31ed050e383cf8201352e4c9276ca249cbecfded0"},
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{file = "torchvision-0.20.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1a31256ff945d64f006bb306813a7c95a531fe16bfb2535c837dd4c104533d7a"},
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{file = "torchvision-0.20.1-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:17cd78adddf81dac57d7dccc9277a4d686425b1c55715f308769770cb26cad5c"},
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{file = "torchvision-0.20.1-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:9f853ba4497ac4691815ad41b523ee23cf5ba4f87b1ce869d704052e233ca8b7"},
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{file = "torchvision-0.20.1-cp312-cp312-win_amd64.whl", hash = "sha256:4a330422c36dbfc946d3a6c1caec3489db07ecdf3675d83369adb2e5a0ca17c4"},
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{file = "torchvision-0.20.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:2cd58406978b813188cf4e9135b218775b57e0bb86d4a88f0339874b8a224819"},
|
||||
{file = "torchvision-0.20.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:408766b2f0ada9e1bc880d12346cec9638535af5df6459ba9ac4ce5c46402f91"},
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{file = "torchvision-0.20.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:abcb8005de8dc393dbd1310ecb669dc68ab664b9107af6d698a6341d1d3f2c3c"},
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{file = "torchvision-0.20.1-cp39-cp39-win_amd64.whl", hash = "sha256:ea9678163bbf19568f4f959d927f3751eeb833cc8eac949de507edde38c1fc9f"},
|
||||
{file = "torchvision-0.21.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:044ea420b8c6c3162a234cada8e2025b9076fa82504758cd11ec5d0f8cd9fa37"},
|
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{file = "torchvision-0.21.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:b0c0b264b89ab572888244f2e0bad5b7eaf5b696068fc0b93e96f7c3c198953f"},
|
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{file = "torchvision-0.21.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:54815e0a56dde95cc6ec952577f67e0dc151eadd928e8d9f6a7f821d69a4a734"},
|
||||
{file = "torchvision-0.21.0-cp310-cp310-win_amd64.whl", hash = "sha256:abbf1d7b9d52c00d2af4afa8dac1fb3e2356f662a4566bd98dfaaa3634f4eb34"},
|
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{file = "torchvision-0.21.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:110d115333524d60e9e474d53c7d20f096dbd8a080232f88dddb90566f90064c"},
|
||||
{file = "torchvision-0.21.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:3891cd086c5071bda6b4ee9d266bb2ac39c998c045c2ebcd1e818b8316fb5d41"},
|
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{file = "torchvision-0.21.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:54454923a50104c66a9ab6bd8b73a11c2fc218c964b1006d5d1fe5b442c3dcb6"},
|
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{file = "torchvision-0.21.0-cp311-cp311-win_amd64.whl", hash = "sha256:49bcfad8cfe2c27dee116c45d4f866d7974bcf14a5a9fbef893635deae322f2f"},
|
||||
{file = "torchvision-0.21.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:97a5814a93c793aaf0179cfc7f916024f4b63218929aee977b645633d074a49f"},
|
||||
{file = "torchvision-0.21.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:b578bcad8a4083b40d34f689b19ca9f7c63e511758d806510ea03c29ac568f7b"},
|
||||
{file = "torchvision-0.21.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:5083a5b1fec2351bf5ea9900a741d54086db75baec4b1d21e39451e00977f1b1"},
|
||||
{file = "torchvision-0.21.0-cp312-cp312-win_amd64.whl", hash = "sha256:6eb75d41e3bbfc2f7642d0abba9383cc9ae6c5a4ca8d6b00628c225e1eaa63b3"},
|
||||
{file = "torchvision-0.21.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:659b76c86757cb2ee4ca2db245e0740cfc3081fef46f0f1064d11adb4a8cee31"},
|
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{file = "torchvision-0.21.0-cp313-cp313-manylinux1_x86_64.whl", hash = "sha256:084ac3f5a1f50c70d630a488d19bf62f323018eae1b1c1232f2b7047d3a7b76d"},
|
||||
{file = "torchvision-0.21.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:5045a3a5f21ec3eea6962fa5f2fa2d4283f854caec25ada493fcf4aab2925467"},
|
||||
{file = "torchvision-0.21.0-cp313-cp313-win_amd64.whl", hash = "sha256:9147f5e096a9270684e3befdee350f3cacafd48e0c54ab195f45790a9c146d67"},
|
||||
{file = "torchvision-0.21.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:5c22caeaae8b3c36d93459f1a5294e6f43306cff856ed243189a229331a404b4"},
|
||||
{file = "torchvision-0.21.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:e6572227228ec521618cea9ac3a368c45b7f96f1f8622dc9f1afe891c044051f"},
|
||||
{file = "torchvision-0.21.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:6bdce3890fa949219de129e85e4f6d544598af3c073afe5c44e14aed15bdcbb2"},
|
||||
{file = "torchvision-0.21.0-cp39-cp39-win_amd64.whl", hash = "sha256:8c44b6924b530d0702e88ff383b65c4b34a0eaf666e8b399a73245574d546947"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = "*"
|
||||
pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
|
||||
torch = "2.5.1"
|
||||
torch = "2.6.0"
|
||||
|
||||
[package.extras]
|
||||
gdown = ["gdown (>=4.7.3)"]
|
||||
@ -7076,21 +7153,18 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
|
||||
|
||||
[[package]]
|
||||
name = "triton"
|
||||
version = "3.1.0"
|
||||
version = "3.2.0"
|
||||
description = "A language and compiler for custom Deep Learning operations"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "triton-3.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6b0dd10a925263abbe9fa37dcde67a5e9b2383fc269fdf59f5657cac38c5d1d8"},
|
||||
{file = "triton-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0f34f6e7885d1bf0eaaf7ba875a5f0ce6f3c13ba98f9503651c1e6dc6757ed5c"},
|
||||
{file = "triton-3.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8182f42fd8080a7d39d666814fa36c5e30cc00ea7eeeb1a2983dbb4c99a0fdc"},
|
||||
{file = "triton-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6dadaca7fc24de34e180271b5cf864c16755702e9f63a16f62df714a8099126a"},
|
||||
{file = "triton-3.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aafa9a20cd0d9fee523cd4504aa7131807a864cd77dcf6efe7e981f18b8c6c11"},
|
||||
{file = "triton-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3e54983cd51875855da7c68ec05c05cf8bb08df361b1d5b69e05e40b0c9bd62"},
|
||||
{file = "triton-3.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8009a1fb093ee8546495e96731336a33fb8856a38e45bb4ab6affd6dbc3ba220"},
|
||||
{file = "triton-3.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8d9b215efc1c26fa7eefb9a157915c92d52e000d2bf83e5f69704047e63f125c"},
|
||||
{file = "triton-3.2.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e5dfa23ba84541d7c0a531dfce76d8bcd19159d50a4a8b14ad01e91734a5c1b0"},
|
||||
{file = "triton-3.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:30ceed0eff2c4a73b14eb63e052992f44bbdf175f3fad21e1ac8097a772de7ee"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
filelock = "*"
|
||||
|
||||
[package.extras]
|
||||
build = ["cmake (>=3.20)", "lit"]
|
||||
tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
|
||||
@ -7497,13 +7571,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "xlsxwriter"
|
||||
version = "3.2.1"
|
||||
version = "3.2.2"
|
||||
description = "A Python module for creating Excel XLSX files."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "XlsxWriter-3.2.1-py3-none-any.whl", hash = "sha256:7e8f7c60b7a1660ef791d46ab5de78469cb978b991ca841af61f5832d2f9f4fe"},
|
||||
{file = "XlsxWriter-3.2.1.tar.gz", hash = "sha256:97618759cb264fb6a93397f660cca156ffa9561743b1823dafb60dc4474e1902"},
|
||||
{file = "XlsxWriter-3.2.2-py3-none-any.whl", hash = "sha256:272ce861e7fa5e82a4a6ebc24511f2cb952fde3461f6c6e1a1e81d3272db1471"},
|
||||
{file = "xlsxwriter-3.2.2.tar.gz", hash = "sha256:befc7f92578a85fed261639fb6cde1fd51b79c5e854040847dde59d4317077dc"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -7761,4 +7835,4 @@ tesserocr = ["tesserocr"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.9"
|
||||
content-hash = "3727fb425795e596dda2c7b5b726eb58fd28ff3c3c3c08e96b6458204ef9f7dc"
|
||||
content-hash = "336970505f4bae6b21f4cf358ebf6b5ef4fa42a4980358297e63bfea381b350a"
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "docling"
|
||||
version = "2.16.0" # DO NOT EDIT, updated automatically
|
||||
version = "2.17.0" # 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"
|
||||
@ -26,7 +26,7 @@ packages = [{include = "docling"}]
|
||||
######################
|
||||
python = "^3.9"
|
||||
pydantic = "^2.0.0"
|
||||
docling-core = { version = "^2.15.1", extras = ["chunking"] }
|
||||
docling-core = {version = "^2.16.1", extras = ["chunking"]}
|
||||
docling-ibm-models = "^3.3.0"
|
||||
deepsearch-glm = "^1.0.0"
|
||||
docling-parse = "^3.1.0"
|
||||
@ -39,7 +39,10 @@ easyocr = "^1.7"
|
||||
tesserocr = { version = "^2.7.1", optional = true }
|
||||
certifi = ">=2024.7.4"
|
||||
rtree = "^1.3.0"
|
||||
scipy = "^1.6.0"
|
||||
scipy = [
|
||||
{ version = "^1.6.0", markers = "python_version >= '3.10'" },
|
||||
{ version = ">=1.6.0,<1.14.0", markers = "python_version < '3.10'" }
|
||||
]
|
||||
typer = "^0.12.5"
|
||||
python-docx = "^1.1.2"
|
||||
python-pptx = "^1.0.2"
|
||||
|
||||
BIN
tests/data/amt_handbook_sample.pdf
Normal file
BIN
tests/data/amt_handbook_sample.pdf
Normal file
Binary file not shown.
BIN
tests/data/docx/unit_test_headers_numbered.docx
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tests/data/docx/unit_test_headers_numbered.docx
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@ -0,0 +1,25 @@
|
||||
<document>
|
||||
<paragraph><location><page_1><loc_12><loc_88><loc_53><loc_94></location>pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.</paragraph>
|
||||
<paragraph><location><page_1><loc_12><loc_77><loc_53><loc_86></location>The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.</paragraph>
|
||||
<subtitle-level-1><location><page_1><loc_12><loc_73><loc_28><loc_75></location>Boots Self-Locking Nut</subtitle-level-1>
|
||||
<paragraph><location><page_1><loc_12><loc_64><loc_54><loc_73></location>The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.</paragraph>
|
||||
<paragraph><location><page_1><loc_12><loc_52><loc_53><loc_62></location>The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.</paragraph>
|
||||
<paragraph><location><page_1><loc_12><loc_38><loc_54><loc_50></location>The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.</paragraph>
|
||||
<paragraph><location><page_1><loc_12><loc_33><loc_53><loc_36></location>Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is</paragraph>
|
||||
<caption><location><page_1><loc_12><loc_8><loc_31><loc_9></location>Figure 7-26. Self-locking nuts.</caption>
|
||||
<figure>
|
||||
<location><page_1><loc_12><loc_10><loc_52><loc_31></location>
|
||||
<caption>Figure 7-26. Self-locking nuts.</caption>
|
||||
</figure>
|
||||
<paragraph><location><page_1><loc_54><loc_85><loc_95><loc_94></location>the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.</paragraph>
|
||||
<paragraph><location><page_1><loc_54><loc_83><loc_55><loc_85></location>.</paragraph>
|
||||
<subtitle-level-1><location><page_1><loc_54><loc_82><loc_76><loc_83></location>Stainless Steel Self-Locking Nut</subtitle-level-1>
|
||||
<paragraph><location><page_1><loc_54><loc_54><loc_96><loc_81></location>The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.</paragraph>
|
||||
<subtitle-level-1><location><page_1><loc_54><loc_51><loc_65><loc_52></location>Elastic Stop Nut</subtitle-level-1>
|
||||
<paragraph><location><page_1><loc_54><loc_47><loc_93><loc_50></location>The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This</paragraph>
|
||||
<caption><location><page_1><loc_54><loc_8><loc_81><loc_10></location>Figure 7-27. Stainless steel self-locking nut.</caption>
|
||||
<figure>
|
||||
<location><page_1><loc_54><loc_11><loc_94><loc_46></location>
|
||||
<caption>Figure 7-27. Stainless steel self-locking nut.</caption>
|
||||
</figure>
|
||||
</document>
|
||||
File diff suppressed because one or more lines are too long
31
tests/data/groundtruth/docling_v1/amt_handbook_sample.md
Normal file
31
tests/data/groundtruth/docling_v1/amt_handbook_sample.md
Normal file
@ -0,0 +1,31 @@
|
||||
pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.
|
||||
|
||||
The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.
|
||||
|
||||
## Boots Self-Locking Nut
|
||||
|
||||
The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.
|
||||
|
||||
The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.
|
||||
|
||||
The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.
|
||||
|
||||
Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is
|
||||
|
||||
Figure 7-26. Self-locking nuts.
|
||||
<!-- image -->
|
||||
|
||||
the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.
|
||||
|
||||
.
|
||||
|
||||
## Stainless Steel Self-Locking Nut
|
||||
|
||||
The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.
|
||||
|
||||
## Elastic Stop Nut
|
||||
|
||||
The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This
|
||||
|
||||
Figure 7-27. Stainless steel self-locking nut.
|
||||
<!-- image -->
|
||||
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@ -141,13 +141,13 @@ tention encoding is then multiplied to the encoded image to produce a feature fo
|
||||
|
||||
The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.
|
||||
|
||||
Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$\_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$\_{box}$ . l$\_{box}$ consists of the generally used l$\_{1}$ loss for object detection and the IoU loss ( l$\_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.
|
||||
Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.
|
||||
|
||||
The loss used to train the TableFormer can be defined as following:
|
||||
|
||||
l$\_{box}$ = λ$\_{iou}$l$\_{iou}$ + λ$\_{l}$$\_{1}$ l = λl$\_{s}$ + (1 - λ ) l$\_{box}$ (1)
|
||||
$$l$\_{box}$ = λ$\_{iou}$l$\_{iou}$ + λ$\_{l}$$_{1}$ l = λl$_{s}$ + (1 - λ ) l$_{box}$ (1)$$
|
||||
|
||||
where λ ∈ [0, 1], and λ$\_{iou}$, λ$\_{l}$$\_{1}$ ∈$\_{R}$ are hyper-parameters.
|
||||
where λ ∈ [0, 1], and λ$\_{iou}$, λ$\_{l}$$_{1}$ ∈$_{R}$ are hyper-parameters.
|
||||
|
||||
## 5. Experimental Results
|
||||
|
||||
@ -155,7 +155,7 @@ where λ ∈ [0, 1], and λ$\_{iou}$, λ$\_{l}$$\_{1}$ ∈$\_{R}$ are hyper-para
|
||||
|
||||
TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:
|
||||
|
||||
Image width and height ≤ 1024 pixels Structural tags length ≤ 512 tokens. (2)
|
||||
$$Image width and height ≤ 1024 pixels Structural tags length ≤ 512 tokens. (2)$$
|
||||
|
||||
Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved
|
||||
|
||||
@ -177,9 +177,9 @@ We also share our baseline results on the challenging SynthTabNet dataset. Throu
|
||||
|
||||
The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:
|
||||
|
||||
TEDS ( T$\_{a}$, T$\_{b}$ ) = 1 - EditDist ( T$\_{a}$, T$\_{b}$ ) max ( | T$\_{a}$ | , | T$\_{b}$ | ) (3)
|
||||
$$TEDS ( T$\_{a}$, T$\_{b}$ ) = 1 - EditDist ( T$\_{a}$, T$\_{b}$ ) max ( | T$\_{a}$ | , | T$\_{b}$ | ) (3)$$
|
||||
|
||||
where T$\_{a}$ and T$\_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .
|
||||
where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .
|
||||
|
||||
## 5.4. Quantitative Analysis
|
||||
|
||||
@ -377,9 +377,9 @@ Here is a step-by-step description of the prediction postprocessing:
|
||||
- 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.
|
||||
- 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:
|
||||
|
||||
alignment = arg min c { D$\_{c}$ } D$\_{c}$ = max { x$\_{c}$ } - min { x$\_{c}$ } (4)
|
||||
$$alignment = arg min c { D$\_{c}$ } D$\_{c}$ = max { x$\_{c}$ } - min { x$\_{c}$ } (4)$$
|
||||
|
||||
where c is one of { left, centroid, right } and x$\_{c}$ is the xcoordinate for the corresponding point.
|
||||
where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.
|
||||
|
||||
- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-
|
||||
|
||||
|
||||
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@ -0,0 +1,23 @@
|
||||
<document>
|
||||
<text><location><page_1><loc_12><loc_88><loc_53><loc_94></location>pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.</text>
|
||||
<text><location><page_1><loc_12><loc_77><loc_53><loc_86></location>The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.</text>
|
||||
<section_header_level_1><location><page_1><loc_12><loc_73><loc_28><loc_75></location>Boots Self-Locking Nut</section_header_level_1>
|
||||
<text><location><page_1><loc_12><loc_64><loc_54><loc_73></location>The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.</text>
|
||||
<text><location><page_1><loc_12><loc_52><loc_53><loc_62></location>The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.</text>
|
||||
<text><location><page_1><loc_12><loc_38><loc_54><loc_50></location>The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.</text>
|
||||
<text><location><page_1><loc_12><loc_33><loc_53><loc_36></location>Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is</text>
|
||||
<figure>
|
||||
<location><page_1><loc_12><loc_10><loc_52><loc_31></location>
|
||||
<caption>Figure 7-26. Self-locking nuts.</caption>
|
||||
</figure>
|
||||
<text><location><page_1><loc_54><loc_85><loc_95><loc_94></location>the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.</text>
|
||||
<text><location><page_1><loc_54><loc_83><loc_55><loc_85></location>.</text>
|
||||
<section_header_level_1><location><page_1><loc_54><loc_82><loc_76><loc_83></location>Stainless Steel Self-Locking Nut</section_header_level_1>
|
||||
<text><location><page_1><loc_54><loc_54><loc_96><loc_81></location>The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.</text>
|
||||
<section_header_level_1><location><page_1><loc_54><loc_51><loc_65><loc_52></location>Elastic Stop Nut</section_header_level_1>
|
||||
<text><location><page_1><loc_54><loc_47><loc_93><loc_50></location>The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This</text>
|
||||
<figure>
|
||||
<location><page_1><loc_54><loc_11><loc_94><loc_46></location>
|
||||
<caption>Figure 7-27. Stainless steel self-locking nut.</caption>
|
||||
</figure>
|
||||
</document>
|
||||
File diff suppressed because one or more lines are too long
33
tests/data/groundtruth/docling_v2/amt_handbook_sample.md
Normal file
33
tests/data/groundtruth/docling_v2/amt_handbook_sample.md
Normal file
@ -0,0 +1,33 @@
|
||||
pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.
|
||||
|
||||
The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.
|
||||
|
||||
## Boots Self-Locking Nut
|
||||
|
||||
The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.
|
||||
|
||||
The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.
|
||||
|
||||
The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.
|
||||
|
||||
Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is
|
||||
|
||||
Figure 7-26. Self-locking nuts.
|
||||
|
||||
<!-- image -->
|
||||
|
||||
the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.
|
||||
|
||||
.
|
||||
|
||||
## Stainless Steel Self-Locking Nut
|
||||
|
||||
The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.
|
||||
|
||||
## Elastic Stop Nut
|
||||
|
||||
The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This
|
||||
|
||||
Figure 7-27. Stainless steel self-locking nut.
|
||||
|
||||
<!-- image -->
|
||||
File diff suppressed because one or more lines are too long
33
tests/data/groundtruth/docling_v2/blocks.md.md
Normal file
33
tests/data/groundtruth/docling_v2/blocks.md.md
Normal file
@ -0,0 +1,33 @@
|
||||
Unordered list:
|
||||
|
||||
- foo
|
||||
|
||||
Empty unordered list:
|
||||
|
||||
Ordered list:
|
||||
|
||||
- bar
|
||||
|
||||
Empty ordered list:
|
||||
|
||||
Heading:
|
||||
|
||||
# my heading
|
||||
|
||||
Empty heading:
|
||||
|
||||
Indented code block:
|
||||
|
||||
```
|
||||
print("Hi!")
|
||||
```
|
||||
|
||||
Empty indented code block:
|
||||
|
||||
Fenced code block:
|
||||
|
||||
```
|
||||
print("Hello world!")
|
||||
```
|
||||
|
||||
Empty fenced code block:
|
||||
@ -16,7 +16,7 @@ Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod
|
||||
|
||||
Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt.
|
||||
|
||||
a 2 + 8 = 12
|
||||
$$a 2 + 8 = 12$$
|
||||
|
||||
Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
|
||||
|
||||
|
||||
File diff suppressed because one or more lines are too long
52
tests/data/groundtruth/docling_v2/duck.md.md
Normal file
52
tests/data/groundtruth/docling_v2/duck.md.md
Normal file
@ -0,0 +1,52 @@
|
||||
Summer activities
|
||||
|
||||
# Swimming in the lake
|
||||
|
||||
Duck
|
||||
|
||||
Figure 1: This is a cute duckling
|
||||
|
||||
## Let’s swim!
|
||||
|
||||
To get started with swimming, first lay down in a water and try not to drown:
|
||||
|
||||
- You can relax and look around
|
||||
- Paddle about
|
||||
- Enjoy summer warmth
|
||||
|
||||
Also, don’t forget:
|
||||
|
||||
- Wear sunglasses
|
||||
- Don’t forget to drink water
|
||||
- Use sun cream
|
||||
|
||||
Hmm, what else…
|
||||
|
||||
## Let’s eat
|
||||
|
||||
After we had a good day of swimming in the lake, it’s important to eat something nice
|
||||
|
||||
I like to eat leaves
|
||||
|
||||
Here are some interesting things a respectful duck could eat:
|
||||
|
||||
| | Food | Calories per portion |
|
||||
|---------|----------------------------------|------------------------|
|
||||
| Leaves | Ash, Elm, Maple | 50 |
|
||||
| Berries | Blueberry, Strawberry, Cranberry | 150 |
|
||||
| Grain | Corn, Buckwheat, Barley | 200 |
|
||||
|
||||
And let’s add another list in the end:
|
||||
|
||||
- Leaves
|
||||
- Berries
|
||||
- Grain
|
||||
|
||||
And here my listing in code:
|
||||
|
||||
```
|
||||
Leaves
|
||||
|
||||
Berries
|
||||
Grain
|
||||
```
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@ -0,0 +1,52 @@
|
||||
item-0 at level 0: unspecified: group _root_
|
||||
item-1 at level 1: title: Test Document
|
||||
item-2 at level 2: paragraph:
|
||||
item-3 at level 2: section_header: Section 1
|
||||
item-4 at level 1: paragraph:
|
||||
item-5 at level 1: paragraph: Paragraph 1.1
|
||||
item-6 at level 1: paragraph:
|
||||
item-7 at level 1: paragraph: Paragraph 1.2
|
||||
item-8 at level 1: paragraph:
|
||||
item-9 at level 1: section: group header-0
|
||||
item-10 at level 2: section: group header-1
|
||||
item-11 at level 3: section_header: Section 1.1
|
||||
item-12 at level 4: paragraph:
|
||||
item-13 at level 4: paragraph: Paragraph 1.1.1
|
||||
item-14 at level 4: paragraph:
|
||||
item-15 at level 4: paragraph: Paragraph 1.1.2
|
||||
item-16 at level 4: paragraph:
|
||||
item-17 at level 3: section_header: Section 1.2
|
||||
item-18 at level 4: paragraph:
|
||||
item-19 at level 4: paragraph: Paragraph 1.1.1
|
||||
item-20 at level 4: paragraph:
|
||||
item-21 at level 4: paragraph: Paragraph 1.1.2
|
||||
item-22 at level 4: paragraph:
|
||||
item-23 at level 4: section_header: Section 1.2.3
|
||||
item-24 at level 5: paragraph:
|
||||
item-25 at level 5: paragraph: Paragraph 1.2.3.1
|
||||
item-26 at level 5: paragraph:
|
||||
item-27 at level 5: paragraph: Paragraph 1.2.3.1
|
||||
item-28 at level 5: paragraph:
|
||||
item-29 at level 5: paragraph:
|
||||
item-30 at level 2: section_header: Section 2
|
||||
item-31 at level 1: paragraph:
|
||||
item-32 at level 1: paragraph: Paragraph 2.1
|
||||
item-33 at level 1: paragraph:
|
||||
item-34 at level 1: paragraph: Paragraph 2.2
|
||||
item-35 at level 1: paragraph:
|
||||
item-36 at level 1: section: group header-0
|
||||
item-37 at level 2: section: group header-1
|
||||
item-38 at level 3: section: group header-2
|
||||
item-39 at level 4: section_header: Section 2.1.1
|
||||
item-40 at level 5: paragraph:
|
||||
item-41 at level 5: paragraph: Paragraph 2.1.1.1
|
||||
item-42 at level 5: paragraph:
|
||||
item-43 at level 5: paragraph: Paragraph 2.1.1.1
|
||||
item-44 at level 5: paragraph:
|
||||
item-45 at level 3: section_header: Section 2.1
|
||||
item-46 at level 4: paragraph:
|
||||
item-47 at level 4: paragraph: Paragraph 2.1.1
|
||||
item-48 at level 4: paragraph:
|
||||
item-49 at level 4: paragraph: Paragraph 2.1.2
|
||||
item-50 at level 4: paragraph:
|
||||
item-51 at level 4: paragraph:
|
||||
@ -0,0 +1,753 @@
|
||||
{
|
||||
"schema_name": "DoclingDocument",
|
||||
"version": "1.0.0",
|
||||
"name": "unit_test_headers_numbered",
|
||||
"origin": {
|
||||
"mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
|
||||
"binary_hash": 7684538628968220703,
|
||||
"filename": "unit_test_headers_numbered.docx"
|
||||
},
|
||||
"furniture": {
|
||||
"self_ref": "#/furniture",
|
||||
"children": [],
|
||||
"name": "_root_",
|
||||
"label": "unspecified"
|
||||
},
|
||||
"body": {
|
||||
"self_ref": "#/body",
|
||||
"children": [
|
||||
{
|
||||
"$ref": "#/texts/0"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/3"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/4"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/5"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/6"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/7"
|
||||
},
|
||||
{
|
||||
"$ref": "#/groups/0"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/28"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/29"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/30"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/31"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/32"
|
||||
},
|
||||
{
|
||||
"$ref": "#/groups/2"
|
||||
}
|
||||
],
|
||||
"name": "_root_",
|
||||
"label": "unspecified"
|
||||
},
|
||||
"groups": [
|
||||
{
|
||||
"self_ref": "#/groups/0",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [
|
||||
{
|
||||
"$ref": "#/groups/1"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/27"
|
||||
}
|
||||
],
|
||||
"name": "header-0",
|
||||
"label": "section"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/groups/1",
|
||||
"parent": {
|
||||
"$ref": "#/groups/0"
|
||||
},
|
||||
"children": [
|
||||
{
|
||||
"$ref": "#/texts/8"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/14"
|
||||
}
|
||||
],
|
||||
"name": "header-1",
|
||||
"label": "section"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/groups/2",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [
|
||||
{
|
||||
"$ref": "#/groups/3"
|
||||
}
|
||||
],
|
||||
"name": "header-0",
|
||||
"label": "section"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/groups/3",
|
||||
"parent": {
|
||||
"$ref": "#/groups/2"
|
||||
},
|
||||
"children": [
|
||||
{
|
||||
"$ref": "#/groups/4"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/39"
|
||||
}
|
||||
],
|
||||
"name": "header-1",
|
||||
"label": "section"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/groups/4",
|
||||
"parent": {
|
||||
"$ref": "#/groups/3"
|
||||
},
|
||||
"children": [
|
||||
{
|
||||
"$ref": "#/texts/33"
|
||||
}
|
||||
],
|
||||
"name": "header-2",
|
||||
"label": "section"
|
||||
}
|
||||
],
|
||||
"texts": [
|
||||
{
|
||||
"self_ref": "#/texts/0",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [
|
||||
{
|
||||
"$ref": "#/texts/1"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/2"
|
||||
}
|
||||
],
|
||||
"label": "title",
|
||||
"prov": [],
|
||||
"orig": "Test Document",
|
||||
"text": "Test Document"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/1",
|
||||
"parent": {
|
||||
"$ref": "#/texts/0"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/2",
|
||||
"parent": {
|
||||
"$ref": "#/texts/0"
|
||||
},
|
||||
"children": [],
|
||||
"label": "section_header",
|
||||
"prov": [],
|
||||
"orig": "Section 1",
|
||||
"text": "Section 1",
|
||||
"level": 1
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/3",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/4",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 1.1",
|
||||
"text": "Paragraph 1.1"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/5",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/6",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 1.2",
|
||||
"text": "Paragraph 1.2"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/7",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/8",
|
||||
"parent": {
|
||||
"$ref": "#/groups/1"
|
||||
},
|
||||
"children": [
|
||||
{
|
||||
"$ref": "#/texts/9"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/10"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/11"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/12"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/13"
|
||||
}
|
||||
],
|
||||
"label": "section_header",
|
||||
"prov": [],
|
||||
"orig": "Section 1.1",
|
||||
"text": "Section 1.1",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/9",
|
||||
"parent": {
|
||||
"$ref": "#/texts/8"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/10",
|
||||
"parent": {
|
||||
"$ref": "#/texts/8"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 1.1.1",
|
||||
"text": "Paragraph 1.1.1"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/11",
|
||||
"parent": {
|
||||
"$ref": "#/texts/8"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/12",
|
||||
"parent": {
|
||||
"$ref": "#/texts/8"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 1.1.2",
|
||||
"text": "Paragraph 1.1.2"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/13",
|
||||
"parent": {
|
||||
"$ref": "#/texts/8"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/14",
|
||||
"parent": {
|
||||
"$ref": "#/groups/1"
|
||||
},
|
||||
"children": [
|
||||
{
|
||||
"$ref": "#/texts/15"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/16"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/17"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/18"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/19"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/20"
|
||||
}
|
||||
],
|
||||
"label": "section_header",
|
||||
"prov": [],
|
||||
"orig": "Section 1.2",
|
||||
"text": "Section 1.2",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/15",
|
||||
"parent": {
|
||||
"$ref": "#/texts/14"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/16",
|
||||
"parent": {
|
||||
"$ref": "#/texts/14"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 1.1.1",
|
||||
"text": "Paragraph 1.1.1"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/17",
|
||||
"parent": {
|
||||
"$ref": "#/texts/14"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/18",
|
||||
"parent": {
|
||||
"$ref": "#/texts/14"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 1.1.2",
|
||||
"text": "Paragraph 1.1.2"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/19",
|
||||
"parent": {
|
||||
"$ref": "#/texts/14"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/20",
|
||||
"parent": {
|
||||
"$ref": "#/texts/14"
|
||||
},
|
||||
"children": [
|
||||
{
|
||||
"$ref": "#/texts/21"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/22"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/23"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/24"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/25"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/26"
|
||||
}
|
||||
],
|
||||
"label": "section_header",
|
||||
"prov": [],
|
||||
"orig": "Section 1.2.3",
|
||||
"text": "Section 1.2.3",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/21",
|
||||
"parent": {
|
||||
"$ref": "#/texts/20"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/22",
|
||||
"parent": {
|
||||
"$ref": "#/texts/20"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 1.2.3.1",
|
||||
"text": "Paragraph 1.2.3.1"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/23",
|
||||
"parent": {
|
||||
"$ref": "#/texts/20"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/24",
|
||||
"parent": {
|
||||
"$ref": "#/texts/20"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 1.2.3.1",
|
||||
"text": "Paragraph 1.2.3.1"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/25",
|
||||
"parent": {
|
||||
"$ref": "#/texts/20"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/26",
|
||||
"parent": {
|
||||
"$ref": "#/texts/20"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/27",
|
||||
"parent": {
|
||||
"$ref": "#/groups/0"
|
||||
},
|
||||
"children": [],
|
||||
"label": "section_header",
|
||||
"prov": [],
|
||||
"orig": "Section 2",
|
||||
"text": "Section 2",
|
||||
"level": 1
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/28",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/29",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 2.1",
|
||||
"text": "Paragraph 2.1"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/30",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/31",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 2.2",
|
||||
"text": "Paragraph 2.2"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/32",
|
||||
"parent": {
|
||||
"$ref": "#/body"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/33",
|
||||
"parent": {
|
||||
"$ref": "#/groups/4"
|
||||
},
|
||||
"children": [
|
||||
{
|
||||
"$ref": "#/texts/34"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/35"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/36"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/37"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/38"
|
||||
}
|
||||
],
|
||||
"label": "section_header",
|
||||
"prov": [],
|
||||
"orig": "Section 2.1.1",
|
||||
"text": "Section 2.1.1",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/34",
|
||||
"parent": {
|
||||
"$ref": "#/texts/33"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/35",
|
||||
"parent": {
|
||||
"$ref": "#/texts/33"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 2.1.1.1",
|
||||
"text": "Paragraph 2.1.1.1"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/36",
|
||||
"parent": {
|
||||
"$ref": "#/texts/33"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/37",
|
||||
"parent": {
|
||||
"$ref": "#/texts/33"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 2.1.1.1",
|
||||
"text": "Paragraph 2.1.1.1"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/38",
|
||||
"parent": {
|
||||
"$ref": "#/texts/33"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/39",
|
||||
"parent": {
|
||||
"$ref": "#/groups/3"
|
||||
},
|
||||
"children": [
|
||||
{
|
||||
"$ref": "#/texts/40"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/41"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/42"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/43"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/44"
|
||||
},
|
||||
{
|
||||
"$ref": "#/texts/45"
|
||||
}
|
||||
],
|
||||
"label": "section_header",
|
||||
"prov": [],
|
||||
"orig": "Section 2.1",
|
||||
"text": "Section 2.1",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/40",
|
||||
"parent": {
|
||||
"$ref": "#/texts/39"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/41",
|
||||
"parent": {
|
||||
"$ref": "#/texts/39"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 2.1.1",
|
||||
"text": "Paragraph 2.1.1"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/42",
|
||||
"parent": {
|
||||
"$ref": "#/texts/39"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/43",
|
||||
"parent": {
|
||||
"$ref": "#/texts/39"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "Paragraph 2.1.2",
|
||||
"text": "Paragraph 2.1.2"
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/44",
|
||||
"parent": {
|
||||
"$ref": "#/texts/39"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
},
|
||||
{
|
||||
"self_ref": "#/texts/45",
|
||||
"parent": {
|
||||
"$ref": "#/texts/39"
|
||||
},
|
||||
"children": [],
|
||||
"label": "paragraph",
|
||||
"prov": [],
|
||||
"orig": "",
|
||||
"text": ""
|
||||
}
|
||||
],
|
||||
"pictures": [],
|
||||
"tables": [],
|
||||
"key_value_items": [],
|
||||
"pages": {}
|
||||
}
|
||||
@ -0,0 +1,43 @@
|
||||
# Test Document
|
||||
|
||||
## Section 1
|
||||
|
||||
Paragraph 1.1
|
||||
|
||||
Paragraph 1.2
|
||||
|
||||
### Section 1.1
|
||||
|
||||
Paragraph 1.1.1
|
||||
|
||||
Paragraph 1.1.2
|
||||
|
||||
### Section 1.2
|
||||
|
||||
Paragraph 1.1.1
|
||||
|
||||
Paragraph 1.1.2
|
||||
|
||||
#### Section 1.2.3
|
||||
|
||||
Paragraph 1.2.3.1
|
||||
|
||||
Paragraph 1.2.3.1
|
||||
|
||||
## Section 2
|
||||
|
||||
Paragraph 2.1
|
||||
|
||||
Paragraph 2.2
|
||||
|
||||
#### Section 2.1.1
|
||||
|
||||
Paragraph 2.1.1.1
|
||||
|
||||
Paragraph 2.1.1.1
|
||||
|
||||
### Section 2.1
|
||||
|
||||
Paragraph 2.1.1
|
||||
|
||||
Paragraph 2.1.2
|
||||
23
tests/data/groundtruth/docling_v2/wiki.md.md
Normal file
23
tests/data/groundtruth/docling_v2/wiki.md.md
Normal file
@ -0,0 +1,23 @@
|
||||
# IBM
|
||||
|
||||
International Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in Armonk, New York and present in over 175 countries.
|
||||
|
||||
It is a publicly traded company and one of the 30 companies in the Dow Jones Industrial Average.
|
||||
|
||||
IBM is the largest industrial research organization in the world, with 19 research facilities across a dozen countries, having held the record for most annual U.S. patents generated by a business for 29 consecutive years from 1993 to 2021.
|
||||
|
||||
IBM was founded in 1911 as the Computing-Tabulating-Recording Company (CTR), a holding company of manufacturers of record-keeping and measuring systems. It was renamed "International Business Machines" in 1924 and soon became the leading manufacturer of punch-card tabulating systems. During the 1960s and 1970s, the IBM mainframe, exemplified by the System/360, was the world's dominant computing platform, with the company producing 80 percent of computers in the U.S. and 70 percent of computers worldwide.[11]
|
||||
|
||||
IBM debuted in the microcomputer market in 1981 with the IBM Personal Computer, — its DOS software provided by Microsoft, — which became the basis for the majority of personal computers to the present day.[12] The company later also found success in the portable space with the ThinkPad. Since the 1990s, IBM has concentrated on computer services, software, supercomputers, and scientific research; it sold its microcomputer division to Lenovo in 2005. IBM continues to develop mainframes, and its supercomputers have consistently ranked among the most powerful in the world in the 21st century.
|
||||
|
||||
As one of the world's oldest and largest technology companies, IBM has been responsible for several technological innovations, including the automated teller machine (ATM), dynamic random-access memory (DRAM), the floppy disk, the hard disk drive, the magnetic stripe card, the relational database, the SQL programming language, and the UPC barcode. The company has made inroads in advanced computer chips, quantum computing, artificial intelligence, and data infrastructure.[13][14][15] IBM employees and alumni have won various recognitions for their scientific research and inventions, including six Nobel Prizes and six Turing Awards.[16]
|
||||
|
||||
## 1910s–1950s
|
||||
|
||||
IBM originated with several technological innovations developed and commercialized in the late 19th century. Julius E. Pitrap patented the computing scale in 1885;[17] Alexander Dey invented the dial recorder (1888);[18] Herman Hollerith patented the Electric Tabulating Machine (1889);[19] and Willard Bundy invented a time clock to record workers' arrival and departure times on a paper tape (1889).[20] On June 16, 1911, their four companies were amalgamated in New York State by Charles Ranlett Flint forming a fifth company, the Computing-Tabulating-Recording Company (CTR) based in Endicott, New York.[1][21] The five companies had 1,300 employees and offices and plants in Endicott and Binghamton, New York; Dayton, Ohio; Detroit, Michigan; Washington, D.C.; and Toronto, Canada.[22]
|
||||
|
||||
Collectively, the companies manufactured a wide array of machinery for sale and lease, ranging from commercial scales and industrial time recorders, meat and cheese slicers, to tabulators and punched cards. Thomas J. Watson, Sr., fired from the National Cash Register Company by John Henry Patterson, called on Flint and, in 1914, was offered a position at CTR.[23] Watson joined CTR as general manager and then, 11 months later, was made President when antitrust cases relating to his time at NCR were resolved.[24] Having learned Patterson's pioneering business practices, Watson proceeded to put the stamp of NCR onto CTR's companies.[23]: 105 He implemented sales conventions, "generous sales incentives, a focus on customer service, an insistence on well-groomed, dark-suited salesmen and had an evangelical fervor for instilling company pride and loyalty in every worker".[25][26] His favorite slogan, "THINK", became a mantra for each company's employees.[25] During Watson's first four years, revenues reached $9 million ($158 million today) and the company's operations expanded to Europe, South America, Asia and Australia.[25] Watson never liked the clumsy hyphenated name "Computing-Tabulating-Recording Company" and chose to replace it with the more expansive title "International Business Machines" which had previously been used as the name of CTR's Canadian Division;[27] the name was changed on February 14, 1924.[28] By 1933, most of the subsidiaries had been merged into one company, IBM.
|
||||
|
||||
## 1960s–1980s
|
||||
|
||||
In 1961, IBM developed the SABRE reservation system for American Airlines and introduced the highly successful Selectric typewriter.
|
||||
43
tests/data/md/blocks.md
Normal file
43
tests/data/md/blocks.md
Normal file
@ -0,0 +1,43 @@
|
||||
Unordered list:
|
||||
|
||||
- foo
|
||||
|
||||
Empty unordered list:
|
||||
|
||||
-
|
||||
|
||||
Ordered list:
|
||||
|
||||
1. bar
|
||||
|
||||
Empty ordered list:
|
||||
|
||||
1.
|
||||
|
||||
Heading:
|
||||
|
||||
# my heading
|
||||
|
||||
Empty heading:
|
||||
|
||||
#
|
||||
|
||||
Indented code block:
|
||||
|
||||
print("Hi!")
|
||||
|
||||
Empty indented code block:
|
||||
|
||||
|
||||
|
||||
Fenced code block:
|
||||
|
||||
```python
|
||||
print("Hello world!")
|
||||
```
|
||||
|
||||
Empty fenced code block:
|
||||
|
||||
```
|
||||
|
||||
```
|
||||
56
tests/data/md/duck.md
Normal file
56
tests/data/md/duck.md
Normal file
@ -0,0 +1,56 @@
|
||||
Summer activities
|
||||
|
||||
# Swimming in the lake
|
||||
|
||||
Duck
|
||||
|
||||
|
||||
Figure 1: This is a cute duckling
|
||||
|
||||
## Let’s swim!
|
||||
|
||||
To get started with swimming, first lay down in a water and try not to drown:
|
||||
|
||||
- You can relax and look around
|
||||
- Paddle about
|
||||
- Enjoy summer warmth
|
||||
|
||||
Also, don’t forget:
|
||||
|
||||
- Wear sunglasses
|
||||
- Don’t forget to drink water
|
||||
- Use sun cream
|
||||
|
||||
Hmm, what else…
|
||||
|
||||
## Let’s eat
|
||||
|
||||
After we had a good day of swimming in the lake,
|
||||
it’s important to eat
|
||||
something nice
|
||||
|
||||
I like to eat leaves
|
||||
|
||||
|
||||
Here are some interesting things a respectful duck could eat:
|
||||
|
||||
| | Food | Calories per portion |
|
||||
|---------|----------------------------------|------------------------|
|
||||
| Leaves | Ash, Elm, Maple | 50 |
|
||||
| Berries | Blueberry, Strawberry, Cranberry | 150 |
|
||||
| Grain | Corn, Buckwheat, Barley | 200 |
|
||||
|
||||
And let’s add another list in the end:
|
||||
|
||||
- Leaves
|
||||
- Berries
|
||||
- Grain
|
||||
|
||||
And here my listing in code:
|
||||
|
||||
```
|
||||
Leaves
|
||||
|
||||
Berries
|
||||
Grain
|
||||
```
|
||||
@ -1 +1 @@
|
||||
[{"page_no": 0, "size": {"width": 595.201171875, "height": 841.9216918945312}, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715733528137207, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715733528137207, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "body": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715733528137207, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "headers": []}}]
|
||||
[{"page_no": 0, "size": {"width": 595.201171875, "height": 841.9216918945312}, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "body": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "headers": []}}]
|
||||
@ -1 +1 @@
|
||||
[{"page_no": 0, "size": {"width": 595.201171875, "height": 841.9216918945312}, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715733528137207, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715733528137207, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "body": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715733528137207, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "headers": []}}]
|
||||
[{"page_no": 0, "size": {"width": 595.201171875, "height": 841.9216918945312}, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "body": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "headers": []}}]
|
||||
35
tests/test_backend_markdown.py
Normal file
35
tests/test_backend_markdown.py
Normal file
@ -0,0 +1,35 @@
|
||||
from pathlib import Path
|
||||
|
||||
from docling.backend.md_backend import MarkdownDocumentBackend
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.document import InputDocument
|
||||
|
||||
|
||||
def test_convert_valid():
|
||||
fmt = InputFormat.MD
|
||||
cls = MarkdownDocumentBackend
|
||||
|
||||
test_data_path = Path("tests") / "data"
|
||||
relevant_paths = sorted((test_data_path / "md").rglob("*.md"))
|
||||
assert len(relevant_paths) > 0
|
||||
|
||||
for in_path in relevant_paths:
|
||||
gt_path = test_data_path / "groundtruth" / "docling_v2" / f"{in_path.name}.md"
|
||||
|
||||
in_doc = InputDocument(
|
||||
path_or_stream=in_path,
|
||||
format=fmt,
|
||||
backend=cls,
|
||||
)
|
||||
backend = cls(
|
||||
in_doc=in_doc,
|
||||
path_or_stream=in_path,
|
||||
)
|
||||
assert backend.is_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
|
||||
@ -3,7 +3,7 @@ import os
|
||||
from pathlib import Path
|
||||
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.document import ConversionResult
|
||||
from docling.datamodel.document import ConversionResult, DoclingDocument
|
||||
from docling.document_converter import DocumentConverter
|
||||
|
||||
GENERATE = False
|
||||
|
||||
@ -6,6 +6,7 @@ from docling.backend.msword_backend import MsWordDocumentBackend
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.document import (
|
||||
ConversionResult,
|
||||
DoclingDocument,
|
||||
InputDocument,
|
||||
SectionHeaderItem,
|
||||
)
|
||||
|
||||
@ -3,7 +3,7 @@ import os
|
||||
from pathlib import Path
|
||||
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.document import ConversionResult
|
||||
from docling.datamodel.document import ConversionResult, DoclingDocument
|
||||
from docling.document_converter import DocumentConverter
|
||||
|
||||
GENERATE = False
|
||||
|
||||
@ -3,7 +3,7 @@ from pathlib import Path
|
||||
from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.document import ConversionResult
|
||||
from docling.datamodel.pipeline_options import PdfPipelineOptions
|
||||
from docling.datamodel.pipeline_options import AcceleratorDevice, PdfPipelineOptions
|
||||
from docling.document_converter import DocumentConverter, PdfFormatOption
|
||||
|
||||
from .verify_utils import verify_conversion_result_v1, verify_conversion_result_v2
|
||||
@ -28,6 +28,7 @@ def get_converter():
|
||||
pipeline_options.do_ocr = False
|
||||
pipeline_options.do_table_structure = True
|
||||
pipeline_options.table_structure_options.do_cell_matching = True
|
||||
pipeline_options.accelerator_options.device = AcceleratorDevice.CPU
|
||||
|
||||
converter = DocumentConverter(
|
||||
format_options={
|
||||
|
||||
@ -6,6 +6,7 @@ from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.document import ConversionResult
|
||||
from docling.datamodel.pipeline_options import (
|
||||
AcceleratorDevice,
|
||||
EasyOcrOptions,
|
||||
OcrMacOptions,
|
||||
OcrOptions,
|
||||
@ -37,6 +38,7 @@ def get_converter(ocr_options: OcrOptions):
|
||||
pipeline_options.do_table_structure = True
|
||||
pipeline_options.table_structure_options.do_cell_matching = True
|
||||
pipeline_options.ocr_options = ocr_options
|
||||
pipeline_options.accelerator_options.device = AcceleratorDevice.CPU
|
||||
|
||||
converter = DocumentConverter(
|
||||
format_options={
|
||||
@ -57,15 +59,18 @@ def test_e2e_conversions():
|
||||
EasyOcrOptions(),
|
||||
TesseractOcrOptions(),
|
||||
TesseractCliOcrOptions(),
|
||||
RapidOcrOptions(),
|
||||
EasyOcrOptions(force_full_page_ocr=True),
|
||||
TesseractOcrOptions(force_full_page_ocr=True),
|
||||
TesseractOcrOptions(force_full_page_ocr=True, lang=["auto"]),
|
||||
TesseractCliOcrOptions(force_full_page_ocr=True),
|
||||
TesseractCliOcrOptions(force_full_page_ocr=True, lang=["auto"]),
|
||||
RapidOcrOptions(force_full_page_ocr=True),
|
||||
]
|
||||
|
||||
# rapidocr is only available for Python >=3.6,<3.13
|
||||
if sys.version_info < (3, 13):
|
||||
engines.append(RapidOcrOptions())
|
||||
engines.append(RapidOcrOptions(force_full_page_ocr=True))
|
||||
|
||||
# only works on mac
|
||||
if "darwin" == sys.platform:
|
||||
engines.append(OcrMacOptions())
|
||||
|
||||
Loading…
Reference in New Issue
Block a user