feat: docling-parse v2 as default PDF backend (#549)
* Move to_docling_document from ds-glm to this repo Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Upgrade to ds-glm 1.0 and docling-parse 3.0 Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Update lock Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Fix DP2 backend code, change CLI default backend Signed-off-by: Christoph Auer <cau@zurich.ibm.com> --------- Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
This commit is contained in:
Christoph Auer
GitHub
parent
9fd2cf847a
commit
aca57f0527
@ -6,7 +6,7 @@ from typing import Iterable, List, Optional, Union
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import pypdfium2 as pdfium
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from docling_core.types.doc import BoundingBox, CoordOrigin, Size
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from docling_parse.docling_parse import pdf_parser_v1
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from docling_parse.pdf_parsers import pdf_parser_v1
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from PIL import Image, ImageDraw
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from pypdfium2 import PdfPage
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@ -6,7 +6,7 @@ from typing import TYPE_CHECKING, Iterable, List, Optional, Union
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import pypdfium2 as pdfium
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from docling_core.types.doc import BoundingBox, CoordOrigin
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from docling_parse.docling_parse import pdf_parser_v2
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from docling_parse.pdf_parsers import pdf_parser_v2
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from PIL import Image, ImageDraw
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from pypdfium2 import PdfPage
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@ -210,12 +210,14 @@ class DoclingParseV2DocumentBackend(PdfDocumentBackend):
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self.parser = pdf_parser_v2("fatal")
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success = False
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if isinstance(path_or_stream, BytesIO):
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if isinstance(self.path_or_stream, BytesIO):
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success = self.parser.load_document_from_bytesio(
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self.document_hash, path_or_stream
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self.document_hash, self.path_or_stream
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)
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elif isinstance(self.path_or_stream, Path):
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success = self.parser.load_document(
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self.document_hash, str(self.path_or_stream)
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)
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elif isinstance(path_or_stream, Path):
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success = self.parser.load_document(self.document_hash, str(path_or_stream))
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if not success:
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raise RuntimeError(
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@ -208,7 +208,7 @@ def convert(
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] = None,
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pdf_backend: Annotated[
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PdfBackend, typer.Option(..., help="The PDF backend to use.")
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] = PdfBackend.DLPARSE_V1,
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] = PdfBackend.DLPARSE_V2,
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table_mode: Annotated[
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TableFormerMode,
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typer.Option(..., help="The mode to use in the table structure model."),
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@ -9,7 +9,7 @@ from pydantic import BaseModel, ConfigDict, model_validator, validate_call
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from docling.backend.abstract_backend import AbstractDocumentBackend
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from docling.backend.asciidoc_backend import AsciiDocBackend
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from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
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from docling.backend.docling_parse_v2_backend import DoclingParseV2DocumentBackend
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from docling.backend.html_backend import HTMLDocumentBackend
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from docling.backend.md_backend import MarkdownDocumentBackend
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from docling.backend.msexcel_backend import MsExcelDocumentBackend
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@ -84,12 +84,12 @@ class HTMLFormatOption(FormatOption):
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class PdfFormatOption(FormatOption):
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pipeline_cls: Type = StandardPdfPipeline
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backend: Type[AbstractDocumentBackend] = DoclingParseDocumentBackend
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backend: Type[AbstractDocumentBackend] = DoclingParseV2DocumentBackend
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class ImageFormatOption(FormatOption):
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pipeline_cls: Type = StandardPdfPipeline
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backend: Type[AbstractDocumentBackend] = DoclingParseDocumentBackend
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backend: Type[AbstractDocumentBackend] = DoclingParseV2DocumentBackend
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def _get_default_option(format: InputFormat) -> FormatOption:
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@ -113,10 +113,10 @@ def _get_default_option(format: InputFormat) -> FormatOption:
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pipeline_cls=SimplePipeline, backend=HTMLDocumentBackend
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),
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InputFormat.IMAGE: FormatOption(
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pipeline_cls=StandardPdfPipeline, backend=DoclingParseDocumentBackend
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pipeline_cls=StandardPdfPipeline, backend=DoclingParseV2DocumentBackend
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),
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InputFormat.PDF: FormatOption(
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pipeline_cls=StandardPdfPipeline, backend=DoclingParseDocumentBackend
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pipeline_cls=StandardPdfPipeline, backend=DoclingParseV2DocumentBackend
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),
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}
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if (options := format_to_default_options.get(format)) is not None:
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@ -4,7 +4,6 @@ from pathlib import Path
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from typing import List, Union
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from deepsearch_glm.nlp_utils import init_nlp_model
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from deepsearch_glm.utils.doc_utils import to_docling_document
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from deepsearch_glm.utils.load_pretrained_models import load_pretrained_nlp_models
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from docling_core.types.doc import BoundingBox, CoordOrigin, DoclingDocument
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from docling_core.types.legacy_doc.base import BoundingBox as DsBoundingBox
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@ -29,6 +28,7 @@ from pydantic import BaseModel, ConfigDict
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from docling.datamodel.base_models import Cluster, FigureElement, Table, TextElement
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from docling.datamodel.document import ConversionResult, layout_label_to_ds_type
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from docling.datamodel.settings import settings
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from docling.utils.glm_utils import to_docling_document
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from docling.utils.profiling import ProfilingScope, TimeRecorder
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from docling.utils.utils import create_hash
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@ -232,7 +232,7 @@ class GlmModel:
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def __call__(self, conv_res: ConversionResult) -> DoclingDocument:
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with TimeRecorder(conv_res, "glm", scope=ProfilingScope.DOCUMENT):
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ds_doc = self._to_legacy_document(conv_res)
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ds_doc_dict = ds_doc.model_dump(by_alias=True)
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ds_doc_dict = ds_doc.model_dump(by_alias=True, exclude_none=True)
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glm_doc = self.model.apply_on_doc(ds_doc_dict)
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336
docling/utils/glm_utils.py
Normal file
336
docling/utils/glm_utils.py
Normal file
@ -0,0 +1,336 @@
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import re
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from pathlib import Path
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from typing import List
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import pandas as pd
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from docling_core.types.doc import (
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BoundingBox,
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CoordOrigin,
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DocItemLabel,
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DoclingDocument,
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DocumentOrigin,
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GroupLabel,
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ProvenanceItem,
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Size,
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TableCell,
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TableData,
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)
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def resolve_item(paths, obj):
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"""Find item in document from a reference path"""
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if len(paths) == 0:
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return obj
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if paths[0] == "#":
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return resolve_item(paths[1:], obj)
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try:
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key = int(paths[0])
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except:
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key = paths[0]
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if len(paths) == 1:
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if isinstance(key, str) and key in obj:
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return obj[key]
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elif isinstance(key, int) and key < len(obj):
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return obj[key]
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else:
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return None
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elif len(paths) > 1:
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if isinstance(key, str) and key in obj:
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return resolve_item(paths[1:], obj[key])
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elif isinstance(key, int) and key < len(obj):
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return resolve_item(paths[1:], obj[key])
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else:
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return None
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else:
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return None
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def _flatten_table_grid(grid: List[List[dict]]) -> List[dict]:
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unique_objects = []
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seen_spans = set()
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for sublist in grid:
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for obj in sublist:
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# Convert the spans list to a tuple of tuples for hashing
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spans_tuple = tuple(tuple(span) for span in obj["spans"])
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if spans_tuple not in seen_spans:
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seen_spans.add(spans_tuple)
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unique_objects.append(obj)
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return unique_objects
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def to_docling_document(doc_glm, update_name_label=False) -> DoclingDocument:
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origin = DocumentOrigin(
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mimetype="application/pdf",
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filename=doc_glm["file-info"]["filename"],
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binary_hash=doc_glm["file-info"]["document-hash"],
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)
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doc_name = Path(origin.filename).stem
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doc: DoclingDocument = DoclingDocument(name=doc_name, origin=origin)
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for page_dim in doc_glm["page-dimensions"]:
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page_no = int(page_dim["page"])
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size = Size(width=page_dim["width"], height=page_dim["height"])
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doc.add_page(page_no=page_no, size=size)
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if "properties" in doc_glm:
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props = pd.DataFrame(
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doc_glm["properties"]["data"], columns=doc_glm["properties"]["headers"]
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)
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else:
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props = pd.DataFrame()
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current_list = None
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for ix, pelem in enumerate(doc_glm["page-elements"]):
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ptype = pelem["type"]
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span_i = pelem["span"][0]
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span_j = pelem["span"][1]
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if "iref" not in pelem:
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# print(json.dumps(pelem, indent=2))
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continue
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iref = pelem["iref"]
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if re.match("#/figures/(\\d+)/captions/(.+)", iref):
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# print(f"skip {iref}")
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continue
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if re.match("#/tables/(\\d+)/captions/(.+)", iref):
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# print(f"skip {iref}")
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continue
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path = iref.split("/")
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obj = resolve_item(path, doc_glm)
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if obj is None:
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current_list = None
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print(f"warning: undefined {path}")
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continue
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if ptype == "figure":
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current_list = None
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text = ""
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caption_refs = []
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for caption in obj["captions"]:
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text += caption["text"]
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for nprov in caption["prov"]:
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npaths = nprov["$ref"].split("/")
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nelem = resolve_item(npaths, doc_glm)
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if nelem is None:
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# print(f"warning: undefined caption {npaths}")
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continue
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span_i = nelem["span"][0]
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span_j = nelem["span"][1]
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cap_text = caption["text"][span_i:span_j]
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# doc_glm["page-elements"].remove(nelem)
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prov = ProvenanceItem(
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page_no=nelem["page"],
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charspan=tuple(nelem["span"]),
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bbox=BoundingBox.from_tuple(
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nelem["bbox"], origin=CoordOrigin.BOTTOMLEFT
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),
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)
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caption_obj = doc.add_text(
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label=DocItemLabel.CAPTION, text=cap_text, prov=prov
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)
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caption_refs.append(caption_obj.get_ref())
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prov = ProvenanceItem(
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page_no=pelem["page"],
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charspan=(0, len(text)),
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bbox=BoundingBox.from_tuple(
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pelem["bbox"], origin=CoordOrigin.BOTTOMLEFT
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),
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)
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pic = doc.add_picture(prov=prov)
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pic.captions.extend(caption_refs)
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_add_child_elements(pic, doc, obj, pelem)
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elif ptype == "table":
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current_list = None
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text = ""
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caption_refs = []
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for caption in obj["captions"]:
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text += caption["text"]
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for nprov in caption["prov"]:
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npaths = nprov["$ref"].split("/")
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nelem = resolve_item(npaths, doc_glm)
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if nelem is None:
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# print(f"warning: undefined caption {npaths}")
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continue
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span_i = nelem["span"][0]
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span_j = nelem["span"][1]
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cap_text = caption["text"][span_i:span_j]
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# doc_glm["page-elements"].remove(nelem)
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prov = ProvenanceItem(
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page_no=nelem["page"],
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charspan=tuple(nelem["span"]),
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bbox=BoundingBox.from_tuple(
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nelem["bbox"], origin=CoordOrigin.BOTTOMLEFT
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),
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)
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caption_obj = doc.add_text(
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label=DocItemLabel.CAPTION, text=cap_text, prov=prov
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)
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caption_refs.append(caption_obj.get_ref())
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table_cells_glm = _flatten_table_grid(obj["data"])
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table_cells = []
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for tbl_cell_glm in table_cells_glm:
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if tbl_cell_glm["bbox"] is not None:
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bbox = BoundingBox.from_tuple(
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tbl_cell_glm["bbox"], origin=CoordOrigin.BOTTOMLEFT
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)
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else:
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bbox = None
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is_col_header = False
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is_row_header = False
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is_row_section = False
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if tbl_cell_glm["type"] == "col_header":
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is_col_header = True
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elif tbl_cell_glm["type"] == "row_header":
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is_row_header = True
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elif tbl_cell_glm["type"] == "row_section":
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is_row_section = True
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table_cells.append(
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TableCell(
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row_span=tbl_cell_glm["row-span"][1]
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- tbl_cell_glm["row-span"][0],
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col_span=tbl_cell_glm["col-span"][1]
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- tbl_cell_glm["col-span"][0],
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start_row_offset_idx=tbl_cell_glm["row-span"][0],
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end_row_offset_idx=tbl_cell_glm["row-span"][1],
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start_col_offset_idx=tbl_cell_glm["col-span"][0],
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end_col_offset_idx=tbl_cell_glm["col-span"][1],
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text=tbl_cell_glm["text"],
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bbox=bbox,
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column_header=is_col_header,
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row_header=is_row_header,
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row_section=is_row_section,
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)
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)
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tbl_data = TableData(
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num_rows=obj.get("#-rows", 0),
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num_cols=obj.get("#-cols", 0),
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table_cells=table_cells,
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)
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prov = ProvenanceItem(
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page_no=pelem["page"],
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charspan=(0, 0),
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bbox=BoundingBox.from_tuple(
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pelem["bbox"], origin=CoordOrigin.BOTTOMLEFT
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),
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)
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tbl = doc.add_table(data=tbl_data, prov=prov)
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tbl.captions.extend(caption_refs)
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elif ptype in ["form", "key_value_region"]:
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label = DocItemLabel(ptype)
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container_el = doc.add_group(label=GroupLabel.UNSPECIFIED, name=label)
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_add_child_elements(container_el, doc, obj, pelem)
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elif "text" in obj:
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text = obj["text"][span_i:span_j]
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type_label = pelem["type"]
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name_label = pelem["name"]
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if update_name_label and len(props) > 0 and type_label == "paragraph":
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prop = props[
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(props["type"] == "semantic") & (props["subj_path"] == iref)
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]
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if len(prop) == 1 and prop.iloc[0]["confidence"] > 0.85:
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name_label = prop.iloc[0]["label"]
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prov = ProvenanceItem(
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page_no=pelem["page"],
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charspan=(0, len(text)),
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bbox=BoundingBox.from_tuple(
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pelem["bbox"], origin=CoordOrigin.BOTTOMLEFT
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),
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)
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label = DocItemLabel(name_label)
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if label == DocItemLabel.LIST_ITEM:
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if current_list is None:
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current_list = doc.add_group(label=GroupLabel.LIST, name="list")
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# TODO: Infer if this is a numbered or a bullet list item
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doc.add_list_item(
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text=text, enumerated=False, prov=prov, parent=current_list
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)
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elif label == DocItemLabel.SECTION_HEADER:
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current_list = None
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doc.add_heading(text=text, prov=prov)
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else:
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current_list = None
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doc.add_text(label=DocItemLabel(name_label), text=text, prov=prov)
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return doc
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def _add_child_elements(container_el, doc, obj, pelem):
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payload = obj.get("payload")
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if payload is not None:
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children = payload.get("children", [])
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for child in children:
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c_label = DocItemLabel(child["label"])
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c_bbox = BoundingBox.model_validate(child["bbox"]).to_bottom_left_origin(
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doc.pages[pelem["page"]].size.height
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)
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c_text = " ".join(
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[
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cell["text"].replace("\x02", "-").strip()
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for cell in child["cells"]
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if len(cell["text"].strip()) > 0
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]
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)
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c_prov = ProvenanceItem(
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page_no=pelem["page"], charspan=(0, len(c_text)), bbox=c_bbox
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)
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if c_label == DocItemLabel.LIST_ITEM:
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# TODO: Infer if this is a numbered or a bullet list item
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doc.add_list_item(parent=container_el, text=c_text, prov=c_prov)
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elif c_label == DocItemLabel.SECTION_HEADER:
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doc.add_heading(parent=container_el, text=c_text, prov=c_prov)
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else:
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doc.add_text(
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parent=container_el, label=c_label, text=c_text, prov=c_prov
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)
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307
poetry.lock
generated
307
poetry.lock
generated
@ -231,6 +231,21 @@ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphi
|
||||
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|
||||
tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
|
||||
|
||||
[[package]]
|
||||
name = "autoflake"
|
||||
version = "2.3.1"
|
||||
description = "Removes unused imports and unused variables"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
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|
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|
||||
[package.dependencies]
|
||||
pyflakes = ">=3.0.0"
|
||||
tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
|
||||
|
||||
[[package]]
|
||||
name = "autopep8"
|
||||
version = "2.2.0"
|
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@ -790,66 +805,49 @@ files = [
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[[package]]
|
||||
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|
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|
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|
||||
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|
||||
|
||||
[package.dependencies]
|
||||
docling-core = ">=2.0,<3.0"
|
||||
docutils = "!=0.21"
|
||||
numpy = ">=1.24.4,<3.0.0"
|
||||
pandas = ">=1.5.1,<3.0.0"
|
||||
python-dotenv = ">=1.0.0,<2.0.0"
|
||||
pywin32 = {version = ">=307,<308", markers = "sys_platform == \"win32\""}
|
||||
requests = ">=2.32.3,<3.0.0"
|
||||
rich = ">=13.7.0,<14.0.0"
|
||||
tabulate = ">=0.8.9"
|
||||
tqdm = ">=4.64.0,<5.0.0"
|
||||
|
||||
[package.extras]
|
||||
docling = ["docling-core (>=2.0,<3.0)", "pandas (>=1.5.1,<3.0.0)"]
|
||||
pyplot = ["matplotlib (>=3.7.1,<4.0.0)"]
|
||||
toolkit = ["deepsearch-toolkit (>=1.1.0,<2.0.0)"]
|
||||
toolkit = ["deepsearch-toolkit (>=1.1.0,<2.0.0)", "python-dotenv (>=1.0.0,<2.0.0)"]
|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "defusedxml"
|
||||
@ -937,52 +935,44 @@ tqdm = ">=4.64.0,<5.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "docling-parse"
|
||||
version = "2.1.2"
|
||||
version = "3.0.0"
|
||||
description = "Simple package to extract text with coordinates from programmatic PDFs"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
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|
||||
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||||
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||||
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{file = "numpy-2.2.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:440cfb3db4c5029775803794f8638fbdbf71ec702caf32735f53b008e1eaece3"},
|
||||
{file = "numpy-2.2.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:a55dc7a7f0b6198b07ec0cd445fbb98b05234e8b00c5ac4874a63372ba98d4ab"},
|
||||
{file = "numpy-2.2.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:4bddbaa30d78c86329b26bd6aaaea06b1e47444da99eddac7bf1e2fab717bd72"},
|
||||
{file = "numpy-2.2.0-cp313-cp313t-win32.whl", hash = "sha256:30bf971c12e4365153afb31fc73f441d4da157153f3400b82db32d04de1e4066"},
|
||||
{file = "numpy-2.2.0-cp313-cp313t-win_amd64.whl", hash = "sha256:d35717333b39d1b6bb8433fa758a55f1081543de527171543a2b710551d40881"},
|
||||
{file = "numpy-2.2.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:e12c6c1ce84628c52d6367863773f7c8c8241be554e8b79686e91a43f1733773"},
|
||||
{file = "numpy-2.2.0-pp310-pypy310_pp73-macosx_14_0_x86_64.whl", hash = "sha256:b6207dc8fb3c8cb5668e885cef9ec7f70189bec4e276f0ff70d5aa078d32c88e"},
|
||||
{file = "numpy-2.2.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a50aeff71d0f97b6450d33940c7181b08be1441c6c193e678211bff11aa725e7"},
|
||||
{file = "numpy-2.2.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:df12a1f99b99f569a7c2ae59aa2d31724e8d835fc7f33e14f4792e3071d11221"},
|
||||
{file = "numpy-2.2.0.tar.gz", hash = "sha256:140dd80ff8981a583a60980be1a655068f8adebf7a45a06a6858c873fcdcd4a0"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -5514,12 +5504,12 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
|
||||
|
||||
[[package]]
|
||||
name = "rapidocr-onnxruntime"
|
||||
version = "1.4.1"
|
||||
version = "1.4.2"
|
||||
description = "A cross platform OCR Library based on OnnxRuntime."
|
||||
optional = true
|
||||
python-versions = "<3.13,>=3.6"
|
||||
files = [
|
||||
{file = "rapidocr_onnxruntime-1.4.1-py3-none-any.whl", hash = "sha256:5ecdb8f4f3beec56630197f87c3e67ab744fce0cc66394b7b1da08c8c96a727f"},
|
||||
{file = "rapidocr_onnxruntime-1.4.2-py3-none-any.whl", hash = "sha256:b921eea8cc8f203588e851d2f26e3ab5e8e9d7c7eec2fe3f7a264ae6275d7e28"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -6071,11 +6061,6 @@ files = [
|
||||
{file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f60021ec1574e56632be2a36b946f8143bf4e5e6af4a06d85281adc22938e0dd"},
|
||||
{file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:394397841449853c2290a32050382edaec3da89e35b3e03d6cc966aebc6a8ae6"},
|
||||
{file = "scikit_learn-1.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:57cc1786cfd6bd118220a92ede80270132aa353647684efa385a74244a41e3b1"},
|
||||
{file = "scikit_learn-1.5.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e9a702e2de732bbb20d3bad29ebd77fc05a6b427dc49964300340e4c9328b3f5"},
|
||||
{file = "scikit_learn-1.5.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:b0768ad641981f5d3a198430a1d31c3e044ed2e8a6f22166b4d546a5116d7908"},
|
||||
{file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:178ddd0a5cb0044464fc1bfc4cca5b1833bfc7bb022d70b05db8530da4bb3dd3"},
|
||||
{file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f7284ade780084d94505632241bf78c44ab3b6f1e8ccab3d2af58e0e950f9c12"},
|
||||
{file = "scikit_learn-1.5.2-cp313-cp313-win_amd64.whl", hash = "sha256:b7b0f9a0b1040830d38c39b91b3a44e1b643f4b36e36567b80b7c6bd2202a27f"},
|
||||
{file = "scikit_learn-1.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:757c7d514ddb00ae249832fe87100d9c73c6ea91423802872d9e74970a0e40b9"},
|
||||
{file = "scikit_learn-1.5.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:52788f48b5d8bca5c0736c175fa6bdaab2ef00a8f536cda698db61bd89c551c1"},
|
||||
{file = "scikit_learn-1.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:643964678f4b5fbdc95cbf8aec638acc7aa70f5f79ee2cdad1eec3df4ba6ead8"},
|
||||
@ -7612,4 +7597,4 @@ tesserocr = ["tesserocr"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.9"
|
||||
content-hash = "c397fcd5c719605f28352cd6e0a3828f082e9684ba64558539e0c3173bdd1fc5"
|
||||
content-hash = "621f8de238fd1f82cfd783531b6ab7c1598378a499c0dcfac323d66bc7ab32ea"
|
||||
|
||||
@ -28,7 +28,7 @@ python = "^3.9"
|
||||
docling-core = { version = "^2.8.0", extras = ["chunking"] }
|
||||
pydantic = "^2.0.0"
|
||||
docling-ibm-models = "^2.0.6"
|
||||
deepsearch-glm = "^0.26.1"
|
||||
deepsearch-glm = "^1.0.0"
|
||||
filetype = "^1.2.0"
|
||||
pypdfium2 = "^4.30.0"
|
||||
pydantic-settings = "^2.3.0"
|
||||
@ -36,7 +36,7 @@ huggingface_hub = ">=0.23,<1"
|
||||
requests = "^2.32.3"
|
||||
easyocr = "^1.7"
|
||||
tesserocr = { version = "^2.7.1", optional = true }
|
||||
docling-parse = "^2.0.5"
|
||||
docling-parse = "^3.0.0"
|
||||
certifi = ">=2024.7.4"
|
||||
rtree = "^1.3.0"
|
||||
scipy = "^1.6.0"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user