From 5e4944f15f0ac1faf3e6a532c3e3ab4da56517a3 Mon Sep 17 00:00:00 2001 From: Christoph Auer <60343111+cau-git@users.noreply.github.com> Date: Fri, 11 Oct 2024 15:12:49 +0200 Subject: [PATCH 01/10] feat: new experimental docling-parse v2 backend (#131) --------- Signed-off-by: Christoph Auer Signed-off-by: Michele Dolfi Co-authored-by: Michele Dolfi --- docling/backend/docling_parse_v2_backend.py | 236 +++ poetry.lock | 1820 ++++++++++--------- pyproject.toml | 2 +- tests/test_backend_docling_parse_v2.py | 65 + 4 files changed, 1304 insertions(+), 819 deletions(-) create mode 100644 docling/backend/docling_parse_v2_backend.py create mode 100644 tests/test_backend_docling_parse_v2.py diff --git a/docling/backend/docling_parse_v2_backend.py b/docling/backend/docling_parse_v2_backend.py new file mode 100644 index 00000000..a4e23c4f --- /dev/null +++ b/docling/backend/docling_parse_v2_backend.py @@ -0,0 +1,236 @@ +import logging +import random +from io import BytesIO +from pathlib import Path +from typing import Iterable, List, Optional, Union + +import pypdfium2 as pdfium +from docling_parse.docling_parse import pdf_parser_v2 +from PIL import Image, ImageDraw +from pypdfium2 import PdfPage + +from docling.backend.abstract_backend import PdfDocumentBackend, PdfPageBackend +from docling.datamodel.base_models import BoundingBox, Cell, CoordOrigin, PageSize + +_log = logging.getLogger(__name__) + + +class DoclingParseV2PageBackend(PdfPageBackend): + def __init__( + self, parser: pdf_parser_v2, document_hash: str, page_no: int, page_obj: PdfPage + ): + self._ppage = page_obj + parsed_page = parser.parse_pdf_from_key_on_page(document_hash, page_no) + + self.valid = "pages" in parsed_page + if self.valid: + self._dpage = parsed_page["pages"][page_no] + else: + _log.info( + f"An error occured when loading page {page_no} of document {document_hash}." + ) + + def is_valid(self) -> bool: + return self.valid + + def get_text_in_rect(self, bbox: BoundingBox) -> str: + if not self.valid: + return "" + # Find intersecting cells on the page + text_piece = "" + page_size = self.get_size() + + parser_width = self._dpage["sanitized"]["dimension"]["width"] + parser_height = self._dpage["sanitized"]["dimension"]["height"] + + scale = ( + 1 # FIX - Replace with param in get_text_in_rect across backends (optional) + ) + + cells_data = self._dpage["sanitized"]["cells"]["data"] + cells_header = self._dpage["sanitized"]["cells"]["header"] + + for i, cell_data in enumerate(cells_data): + x0 = cell_data[cells_header.index("x0")] + y0 = cell_data[cells_header.index("y0")] + x1 = cell_data[cells_header.index("x1")] + y1 = cell_data[cells_header.index("y1")] + + cell_bbox = BoundingBox( + l=x0 * scale * page_size.width / parser_width, + b=y0 * scale * page_size.height / parser_height, + r=x1 * scale * page_size.width / parser_width, + t=y1 * scale * page_size.height / parser_height, + coord_origin=CoordOrigin.BOTTOMLEFT, + ).to_top_left_origin(page_height=page_size.height * scale) + + overlap_frac = cell_bbox.intersection_area_with(bbox) / cell_bbox.area() + + if overlap_frac > 0.5: + if len(text_piece) > 0: + text_piece += " " + text_piece += cell_data[cells_header.index("text")] + + return text_piece + + def get_text_cells(self) -> Iterable[Cell]: + cells: List[Cell] = [] + cell_counter = 0 + + if not self.valid: + return cells + + page_size = self.get_size() + + parser_width = self._dpage["sanitized"]["dimension"]["width"] + parser_height = self._dpage["sanitized"]["dimension"]["height"] + + cells_data = self._dpage["sanitized"]["cells"]["data"] + cells_header = self._dpage["sanitized"]["cells"]["header"] + + for i, cell_data in enumerate(cells_data): + x0 = cell_data[cells_header.index("x0")] + y0 = cell_data[cells_header.index("y0")] + x1 = cell_data[cells_header.index("x1")] + y1 = cell_data[cells_header.index("y1")] + + if x1 < x0: + x0, x1 = x1, x0 + if y1 < y0: + y0, y1 = y1, y0 + + text_piece = cell_data[cells_header.index("text")] + cells.append( + Cell( + id=cell_counter, + text=text_piece, + bbox=BoundingBox( + # l=x0, b=y0, r=x1, t=y1, + l=x0 * page_size.width / parser_width, + b=y0 * page_size.height / parser_height, + r=x1 * page_size.width / parser_width, + t=y1 * page_size.height / parser_height, + coord_origin=CoordOrigin.BOTTOMLEFT, + ).to_top_left_origin(page_size.height), + ) + ) + cell_counter += 1 + + def draw_clusters_and_cells(): + image = ( + self.get_page_image() + ) # make new image to avoid drawing on the saved ones + draw = ImageDraw.Draw(image) + for c in cells: + x0, y0, x1, y1 = c.bbox.as_tuple() + cell_color = ( + random.randint(30, 140), + random.randint(30, 140), + random.randint(30, 140), + ) + draw.rectangle([(x0, y0), (x1, y1)], outline=cell_color) + image.show() + + # draw_clusters_and_cells() + + return cells + + def get_bitmap_rects(self, scale: float = 1) -> Iterable[BoundingBox]: + AREA_THRESHOLD = 32 * 32 + + images = self._dpage["sanitized"]["images"]["data"] + images_header = self._dpage["sanitized"]["images"]["header"] + + for row in images: + x0 = row[images_header.index("x0")] + y0 = row[images_header.index("y0")] + x1 = row[images_header.index("x1")] + y1 = row[images_header.index("y1")] + + cropbox = BoundingBox.from_tuple( + (x0, y0, x1, y1), origin=CoordOrigin.BOTTOMLEFT + ).to_top_left_origin(self.get_size().height) + + if cropbox.area() > AREA_THRESHOLD: + cropbox = cropbox.scaled(scale=scale) + + yield cropbox + + def get_page_image( + self, scale: float = 1, cropbox: Optional[BoundingBox] = None + ) -> Image.Image: + + page_size = self.get_size() + + if not cropbox: + cropbox = BoundingBox( + l=0, + r=page_size.width, + t=0, + b=page_size.height, + coord_origin=CoordOrigin.TOPLEFT, + ) + padbox = BoundingBox( + l=0, r=0, t=0, b=0, coord_origin=CoordOrigin.BOTTOMLEFT + ) + else: + padbox = cropbox.to_bottom_left_origin(page_size.height) + padbox.r = page_size.width - padbox.r + padbox.t = page_size.height - padbox.t + + image = ( + self._ppage.render( + scale=scale * 1.5, + rotation=0, # no additional rotation + crop=padbox.as_tuple(), + ) + .to_pil() + .resize(size=(round(cropbox.width * scale), round(cropbox.height * scale))) + ) # We resize the image from 1.5x the given scale to make it sharper. + + return image + + def get_size(self) -> PageSize: + return PageSize(width=self._ppage.get_width(), height=self._ppage.get_height()) + + def unload(self): + self._ppage = None + self._dpage = None + + +class DoclingParseV2DocumentBackend(PdfDocumentBackend): + def __init__(self, path_or_stream: Union[BytesIO, Path], document_hash: str): + super().__init__(path_or_stream, document_hash) + + self._pdoc = pdfium.PdfDocument(path_or_stream) + self.parser = pdf_parser_v2("fatal") + + success = False + if isinstance(path_or_stream, BytesIO): + success = self.parser.load_document_from_bytesio( + document_hash, path_or_stream + ) + elif isinstance(path_or_stream, Path): + success = self.parser.load_document(document_hash, str(path_or_stream)) + + if not success: + raise RuntimeError( + f"docling-parse could not load document {document_hash}." + ) + + def page_count(self) -> int: + return len(self._pdoc) # To be replaced with docling-parse API + + def load_page(self, page_no: int) -> DoclingParseV2PageBackend: + return DoclingParseV2PageBackend( + self.parser, self.document_hash, page_no, self._pdoc[page_no] + ) + + def is_valid(self) -> bool: + return self.page_count() > 0 + + def unload(self): + super().unload() + self.parser.unload_document(self.document_hash) + self._pdoc.close() + self._pdoc = None diff --git a/poetry.lock b/poetry.lock index 0e3f4319..307927dd 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,113 +2,113 @@ [[package]] name = "aiohappyeyeballs" -version = "2.4.0" +version = "2.4.3" description = "Happy Eyeballs for asyncio" optional = false python-versions = ">=3.8" files = [ - {file = "aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = 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+47,7 @@ huggingface_hub = ">=0.23,<1" requests = "^2.32.3" easyocr = "^1.7" tesserocr = { version = "^2.7.1", optional = true } -docling-parse = "^1.4.1" +docling-parse = "^1.6.0" certifi = ">=2024.7.4" rtree = "^1.3.0" scipy = "^1.14.1" diff --git a/tests/test_backend_docling_parse_v2.py b/tests/test_backend_docling_parse_v2.py new file mode 100644 index 00000000..b4c4be0c --- /dev/null +++ b/tests/test_backend_docling_parse_v2.py @@ -0,0 +1,65 @@ +from pathlib import Path + +import pytest + +from docling.backend.docling_parse_v2_backend import ( + DoclingParseV2DocumentBackend, + DoclingParseV2PageBackend, +) +from docling.datamodel.base_models import BoundingBox + + +@pytest.fixture +def test_doc_path(): + return Path("./tests/data/2206.01062.pdf") + + +@pytest.mark.skip +def test_text_cell_counts(): + pdf_doc = Path("./tests/data/redp5695.pdf") + + doc_backend = DoclingParseV2DocumentBackend(pdf_doc, "123456xyz") + + for page_index in range(0, doc_backend.page_count()): + last_cell_count = None + for i in range(10): + page_backend: DoclingParseV2PageBackend = doc_backend.load_page(0) + cells = list(page_backend.get_text_cells()) + + if last_cell_count is None: + last_cell_count = len(cells) + + if len(cells) != last_cell_count: + assert ( + False + ), "Loading page multiple times yielded non-identical text cell counts" + last_cell_count = len(cells) + + +def test_get_text_from_rect(test_doc_path): + doc_backend = DoclingParseV2DocumentBackend(test_doc_path, "123456xyz") + page_backend: DoclingParseV2PageBackend = doc_backend.load_page(0) + + # Get the title text of the DocLayNet paper + textpiece = page_backend.get_text_in_rect( + bbox=BoundingBox(l=102, t=77, r=511, b=124) + ) + ref = "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis" + + assert textpiece.strip() == ref + + +def test_crop_page_image(test_doc_path): + doc_backend = DoclingParseV2DocumentBackend(test_doc_path, "123456xyz") + page_backend: DoclingParseV2PageBackend = doc_backend.load_page(0) + + # Crop out "Figure 1" from the DocLayNet paper + im = page_backend.get_page_image( + scale=2, cropbox=BoundingBox(l=317, t=246, r=574, b=527) + ) + # im.show() + + +def test_num_pages(test_doc_path): + doc_backend = DoclingParseV2DocumentBackend(test_doc_path, "123456xyz") + doc_backend.page_count() == 9 From 4672b24c1a1de4fadef3ea383083653f9a74b13d Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Fri, 11 Oct 2024 13:48:02 +0000 Subject: [PATCH 02/10] chore: bump version to 1.20.0 [skip ci] --- CHANGELOG.md | 6 ++++++ pyproject.toml | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index c45dc657..c3ecad46 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,9 @@ +## [v1.20.0](https://github.com/DS4SD/docling/releases/tag/v1.20.0) - 2024-10-11 + +### Feature + +* New experimental docling-parse v2 backend ([#131](https://github.com/DS4SD/docling/issues/131)) ([`5e4944f`](https://github.com/DS4SD/docling/commit/5e4944f15f0ac1faf3e6a532c3e3ab4da56517a3)) + ## [v1.19.1](https://github.com/DS4SD/docling/releases/tag/v1.19.1) - 2024-10-11 ### Fix diff --git a/pyproject.toml b/pyproject.toml index fb6a4032..015cb384 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "docling" -version = "1.19.1" # DO NOT EDIT, updated automatically +version = "1.20.0" # DO NOT EDIT, updated automatically description = "Docling PDF conversion package" authors = ["Christoph Auer ", "Michele Dolfi ", "Maxim Lysak ", "Nikos Livathinos ", "Ahmed Nassar ", "Peter Staar "] license = "MIT" From 2b1e72d327311b5c361cc923df304aa9f42d46be Mon Sep 17 00:00:00 2001 From: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> Date: Mon, 14 Oct 2024 08:40:22 +0200 Subject: [PATCH 03/10] refactor: fix type of tesseractocr options (#140) Signed-off-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> --- docling/models/tesseract_ocr_model.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docling/models/tesseract_ocr_model.py b/docling/models/tesseract_ocr_model.py index 1b4f6f7f..2b616ca8 100644 --- a/docling/models/tesseract_ocr_model.py +++ b/docling/models/tesseract_ocr_model.py @@ -4,16 +4,16 @@ from typing import Iterable import numpy from docling.datamodel.base_models import BoundingBox, CoordOrigin, OcrCell, Page -from docling.datamodel.pipeline_options import TesseractCliOcrOptions +from docling.datamodel.pipeline_options import TesseractOcrOptions from docling.models.base_ocr_model import BaseOcrModel _log = logging.getLogger(__name__) class TesseractOcrModel(BaseOcrModel): - def __init__(self, enabled: bool, options: TesseractCliOcrOptions): + def __init__(self, enabled: bool, options: TesseractOcrOptions): super().__init__(enabled=enabled, options=options) - self.options: TesseractCliOcrOptions + self.options: TesseractOcrOptions self.scale = 3 # multiplier for 72 dpi == 216 dpi. self.reader = None From d504432c1ee250ea417e8239ff5c16c5bec5a2c7 Mon Sep 17 00:00:00 2001 From: Panos Vagenas <35837085+vagenas@users.noreply.github.com> Date: Mon, 14 Oct 2024 14:13:13 +0200 Subject: [PATCH 04/10] docs: introduce docs site (#141) Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> --- README.md | 6 +- logo.png => docs/assets/logo.png | Bin logo.svg => docs/assets/logo.svg | 0 docs/examples/batch_convert.py | 105 ++++ docs/examples/custom_convert.py | 175 +++++++ docs/examples/export_figures.py | 85 +++ docs/examples/export_multimodal.py | 116 +++++ docs/examples/export_tables.py | 74 +++ docs/examples/minimal.py | 6 + .../examples}/rag_langchain.ipynb | 2 +- .../examples}/rag_llamaindex.ipynb | 8 +- docs/index.md | 29 ++ docs/installation.md | 100 ++++ docs/integrations/llamaindex.md | 25 + docs/overrides/main.html | 7 + docs/stylesheets/extra.css | 3 + examples/batch_convert.py | 106 +--- examples/custom_convert.py | 176 +------ examples/export_figures.py | 86 +-- examples/export_multimodal.py | 117 +---- examples/export_tables.py | 75 +-- examples/minimal.py | 7 +- mkdocs.yml | 97 ++++ poetry.lock | 491 +++++++++++++++++- pyproject.toml | 2 + 25 files changed, 1324 insertions(+), 574 deletions(-) rename logo.png => docs/assets/logo.png (100%) rename logo.svg => docs/assets/logo.svg (100%) create mode 100644 docs/examples/batch_convert.py create mode 100644 docs/examples/custom_convert.py create mode 100644 docs/examples/export_figures.py create mode 100644 docs/examples/export_multimodal.py create mode 100644 docs/examples/export_tables.py create mode 100644 docs/examples/minimal.py rename {examples => docs/examples}/rag_langchain.ipynb (99%) rename {examples => docs/examples}/rag_llamaindex.ipynb (98%) create mode 100644 docs/index.md create mode 100644 docs/installation.md create mode 100644 docs/integrations/llamaindex.md create mode 100644 docs/overrides/main.html create mode 100644 docs/stylesheets/extra.css mode change 100644 => 120000 examples/batch_convert.py mode change 100644 => 120000 examples/custom_convert.py mode change 100644 => 120000 examples/export_figures.py mode change 100644 => 120000 examples/export_multimodal.py mode change 100644 => 120000 examples/export_tables.py mode change 100644 => 120000 examples/minimal.py create mode 100644 mkdocs.yml diff --git a/README.md b/README.md index df5c9a76..aedea2d0 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@

- Docling + Docling

@@ -200,8 +200,8 @@ To see all available options (export formats etc.) run `docling --help`. ### RAG Check out the following examples showcasing RAG using Docling with standard LLM application frameworks: -- [Basic RAG pipeline with 🦙 LlamaIndex](https://github.com/DS4SD/docling/tree/main/examples/rag_llamaindex.ipynb) -- [Basic RAG pipeline with 🦜🔗 LangChain](https://github.com/DS4SD/docling/tree/main/examples/rag_langchain.ipynb) +- [Basic RAG pipeline with LlamaIndex 🦙](https://github.com/DS4SD/docling/tree/main/docs/examples/rag_llamaindex.ipynb) +- [Basic RAG pipeline with LangChain 🦜🔗](https://github.com/DS4SD/docling/tree/main/docs/examples/rag_langchain.ipynb) ## Advanced features diff --git a/logo.png b/docs/assets/logo.png similarity index 100% rename from logo.png rename to docs/assets/logo.png diff --git a/logo.svg b/docs/assets/logo.svg similarity index 100% rename from logo.svg rename to docs/assets/logo.svg diff --git a/docs/examples/batch_convert.py b/docs/examples/batch_convert.py new file mode 100644 index 00000000..2f3f38b1 --- /dev/null +++ b/docs/examples/batch_convert.py @@ -0,0 +1,105 @@ +import json +import logging +import time +from pathlib import Path +from typing import Iterable + +from docling.datamodel.base_models import ConversionStatus +from docling.datamodel.document import ConversionResult, DocumentConversionInput +from docling.document_converter import DocumentConverter + +_log = logging.getLogger(__name__) + + +def export_documents( + conv_results: Iterable[ConversionResult], + output_dir: Path, +): + output_dir.mkdir(parents=True, exist_ok=True) + + success_count = 0 + failure_count = 0 + partial_success_count = 0 + + for conv_res in conv_results: + if conv_res.status == ConversionStatus.SUCCESS: + success_count += 1 + doc_filename = conv_res.input.file.stem + + # Export Deep Search document JSON format: + with (output_dir / f"{doc_filename}.json").open( + "w", encoding="utf-8" + ) as fp: + fp.write(json.dumps(conv_res.render_as_dict())) + + # Export Text format: + with (output_dir / f"{doc_filename}.txt").open("w", encoding="utf-8") as fp: + fp.write(conv_res.render_as_text()) + + # Export Markdown format: + with (output_dir / f"{doc_filename}.md").open("w", encoding="utf-8") as fp: + fp.write(conv_res.render_as_markdown()) + + # Export Document Tags format: + with (output_dir / f"{doc_filename}.doctags").open( + "w", encoding="utf-8" + ) as fp: + fp.write(conv_res.render_as_doctags()) + + elif conv_res.status == ConversionStatus.PARTIAL_SUCCESS: + _log.info( + f"Document {conv_res.input.file} was partially converted with the following errors:" + ) + for item in conv_res.errors: + _log.info(f"\t{item.error_message}") + partial_success_count += 1 + else: + _log.info(f"Document {conv_res.input.file} failed to convert.") + failure_count += 1 + + _log.info( + f"Processed {success_count + partial_success_count + failure_count} docs, " + f"of which {failure_count} failed " + f"and {partial_success_count} were partially converted." + ) + return success_count, partial_success_count, failure_count + + +def main(): + logging.basicConfig(level=logging.INFO) + + input_doc_paths = [ + Path("./tests/data/2206.01062.pdf"), + Path("./tests/data/2203.01017v2.pdf"), + Path("./tests/data/2305.03393v1.pdf"), + Path("./tests/data/redp5110.pdf"), + Path("./tests/data/redp5695.pdf"), + ] + + # buf = BytesIO(Path("./test/data/2206.01062.pdf").open("rb").read()) + # docs = [DocumentStream(filename="my_doc.pdf", stream=buf)] + # input = DocumentConversionInput.from_streams(docs) + + doc_converter = DocumentConverter() + + input = DocumentConversionInput.from_paths(input_doc_paths) + + start_time = time.time() + + conv_results = doc_converter.convert(input) + success_count, partial_success_count, failure_count = export_documents( + conv_results, output_dir=Path("./scratch") + ) + + end_time = time.time() - start_time + + _log.info(f"All documents were converted in {end_time:.2f} seconds.") + + if failure_count > 0: + raise RuntimeError( + f"The example failed converting {failure_count} on {len(input_doc_paths)}." + ) + + +if __name__ == "__main__": + main() diff --git a/docs/examples/custom_convert.py b/docs/examples/custom_convert.py new file mode 100644 index 00000000..e386bb3a --- /dev/null +++ b/docs/examples/custom_convert.py @@ -0,0 +1,175 @@ +import json +import logging +import time +from pathlib import Path +from typing import Iterable + +from docling.backend.docling_parse_backend import DoclingParseDocumentBackend +from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend +from docling.datamodel.base_models import ConversionStatus, PipelineOptions +from docling.datamodel.document import ConversionResult, DocumentConversionInput +from docling.datamodel.pipeline_options import ( + TesseractCliOcrOptions, + TesseractOcrOptions, +) +from docling.document_converter import DocumentConverter + +_log = logging.getLogger(__name__) + + +def export_documents( + conv_results: Iterable[ConversionResult], + output_dir: Path, +): + output_dir.mkdir(parents=True, exist_ok=True) + + success_count = 0 + failure_count = 0 + + for conv_res in conv_results: + if conv_res.status == ConversionStatus.SUCCESS: + success_count += 1 + doc_filename = conv_res.input.file.stem + + # Export Deep Search document JSON format: + with (output_dir / f"{doc_filename}.json").open( + "w", encoding="utf-8" + ) as fp: + fp.write(json.dumps(conv_res.render_as_dict())) + + # Export Text format: + with (output_dir / f"{doc_filename}.txt").open("w", encoding="utf-8") as fp: + fp.write(conv_res.render_as_text()) + + # Export Markdown format: + with (output_dir / f"{doc_filename}.md").open("w", encoding="utf-8") as fp: + fp.write(conv_res.render_as_markdown()) + + # Export Document Tags format: + with (output_dir / f"{doc_filename}.doctags").open( + "w", encoding="utf-8" + ) as fp: + fp.write(conv_res.render_as_doctags()) + + else: + _log.info(f"Document {conv_res.input.file} failed to convert.") + failure_count += 1 + + _log.info( + f"Processed {success_count + failure_count} docs, of which {failure_count} failed" + ) + + return success_count, failure_count + + +def main(): + logging.basicConfig(level=logging.INFO) + + input_doc_paths = [ + Path("./tests/data/2206.01062.pdf"), + ] + + ########################################################################### + + # The following sections contain a combination of PipelineOptions + # and PDF Backends for various configurations. + # Uncomment one section at the time to see the differences in the output. + + # PyPdfium without EasyOCR + # -------------------- + # pipeline_options = PipelineOptions() + # pipeline_options.do_ocr=False + # pipeline_options.do_table_structure=True + # pipeline_options.table_structure_options.do_cell_matching = False + + # doc_converter = DocumentConverter( + # pipeline_options=pipeline_options, + # pdf_backend=PyPdfiumDocumentBackend, + # ) + + # PyPdfium with EasyOCR + # ----------------- + # pipeline_options = PipelineOptions() + # pipeline_options.do_ocr=True + # pipeline_options.do_table_structure=True + # pipeline_options.table_structure_options.do_cell_matching = True + + # doc_converter = DocumentConverter( + # pipeline_options=pipeline_options, + # pdf_backend=PyPdfiumDocumentBackend, + # ) + + # Docling Parse without EasyOCR + # ------------------------- + pipeline_options = PipelineOptions() + pipeline_options.do_ocr = False + pipeline_options.do_table_structure = True + pipeline_options.table_structure_options.do_cell_matching = True + + doc_converter = DocumentConverter( + pipeline_options=pipeline_options, + pdf_backend=DoclingParseDocumentBackend, + ) + + # Docling Parse with EasyOCR + # ---------------------- + # pipeline_options = PipelineOptions() + # pipeline_options.do_ocr=True + # pipeline_options.do_table_structure=True + # pipeline_options.table_structure_options.do_cell_matching = True + + # doc_converter = DocumentConverter( + # pipeline_options=pipeline_options, + # pdf_backend=DoclingParseDocumentBackend, + # ) + + # Docling Parse with Tesseract + # ---------------------- + # pipeline_options = PipelineOptions() + # pipeline_options.do_ocr = True + # pipeline_options.do_table_structure = True + # pipeline_options.table_structure_options.do_cell_matching = True + # pipeline_options.ocr_options = TesseractOcrOptions() + + # doc_converter = DocumentConverter( + # pipeline_options=pipeline_options, + # pdf_backend=DoclingParseDocumentBackend, + # ) + + # Docling Parse with Tesseract CLI + # ---------------------- + # pipeline_options = PipelineOptions() + # pipeline_options.do_ocr = True + # pipeline_options.do_table_structure = True + # pipeline_options.table_structure_options.do_cell_matching = True + # pipeline_options.ocr_options = TesseractCliOcrOptions() + + # doc_converter = DocumentConverter( + # pipeline_options=pipeline_options, + # pdf_backend=DoclingParseDocumentBackend, + # ) + + ########################################################################### + + # Define input files + input = DocumentConversionInput.from_paths(input_doc_paths) + + start_time = time.time() + + conv_results = doc_converter.convert(input) + success_count, failure_count = export_documents( + conv_results, output_dir=Path("./scratch") + ) + + end_time = time.time() - start_time + + _log.info(f"All documents were converted in {end_time:.2f} seconds.") + + if failure_count > 0: + raise RuntimeError( + f"The example failed converting {failure_count} on {len(input_doc_paths)}." + ) + + +if __name__ == "__main__": + main() diff --git a/docs/examples/export_figures.py b/docs/examples/export_figures.py new file mode 100644 index 00000000..bdffbec1 --- /dev/null +++ b/docs/examples/export_figures.py @@ -0,0 +1,85 @@ +import logging +import time +from pathlib import Path +from typing import Tuple + +from docling.datamodel.base_models import ( + AssembleOptions, + ConversionStatus, + FigureElement, + PageElement, + TableElement, +) +from docling.datamodel.document import DocumentConversionInput +from docling.document_converter import DocumentConverter + +_log = logging.getLogger(__name__) + +IMAGE_RESOLUTION_SCALE = 2.0 + + +def main(): + logging.basicConfig(level=logging.INFO) + + input_doc_paths = [ + Path("./tests/data/2206.01062.pdf"), + ] + output_dir = Path("./scratch") + + input_files = DocumentConversionInput.from_paths(input_doc_paths) + + # Important: For operating with page images, we must keep them, otherwise the DocumentConverter + # will destroy them for cleaning up memory. + # This is done by setting AssembleOptions.images_scale, which also defines the scale of images. + # scale=1 correspond of a standard 72 DPI image + assemble_options = AssembleOptions() + assemble_options.images_scale = IMAGE_RESOLUTION_SCALE + + doc_converter = DocumentConverter(assemble_options=assemble_options) + + start_time = time.time() + + conv_results = doc_converter.convert(input_files) + + success_count = 0 + failure_count = 0 + output_dir.mkdir(parents=True, exist_ok=True) + for conv_res in conv_results: + if conv_res.status != ConversionStatus.SUCCESS: + _log.info(f"Document {conv_res.input.file} failed to convert.") + failure_count += 1 + continue + + doc_filename = conv_res.input.file.stem + + # Export page images + for page in conv_res.pages: + page_no = page.page_no + 1 + page_image_filename = output_dir / f"{doc_filename}-{page_no}.png" + with page_image_filename.open("wb") as fp: + page.image.save(fp, format="PNG") + + # Export figures and tables + for element, image in conv_res.render_element_images( + element_types=(FigureElement, TableElement) + ): + element_image_filename = ( + output_dir / f"{doc_filename}-element-{element.id}.png" + ) + with element_image_filename.open("wb") as fp: + image.save(fp, "PNG") + + success_count += 1 + + end_time = time.time() - start_time + + _log.info(f"All documents were converted in {end_time:.2f} seconds.") + + if failure_count > 0: + raise RuntimeError( + f"The example failed converting {failure_count} on {len(input_doc_paths)}." + ) + + +if __name__ == "__main__": + main() diff --git a/docs/examples/export_multimodal.py b/docs/examples/export_multimodal.py new file mode 100644 index 00000000..7c016b19 --- /dev/null +++ b/docs/examples/export_multimodal.py @@ -0,0 +1,116 @@ +import datetime +import logging +import time +from pathlib import Path + +import pandas as pd + +from docling.datamodel.base_models import AssembleOptions, ConversionStatus +from docling.datamodel.document import DocumentConversionInput +from docling.document_converter import DocumentConverter +from docling.utils.export import generate_multimodal_pages + +_log = logging.getLogger(__name__) + +IMAGE_RESOLUTION_SCALE = 2.0 + + +def main(): + logging.basicConfig(level=logging.INFO) + + input_doc_paths = [ + Path("./tests/data/2206.01062.pdf"), + ] + output_dir = Path("./scratch") + + input_files = DocumentConversionInput.from_paths(input_doc_paths) + + # Important: For operating with page images, we must keep them, otherwise the DocumentConverter + # will destroy them for cleaning up memory. + # This is done by setting AssembleOptions.images_scale, which also defines the scale of images. + # scale=1 correspond of a standard 72 DPI image + assemble_options = AssembleOptions() + assemble_options.images_scale = IMAGE_RESOLUTION_SCALE + + doc_converter = DocumentConverter(assemble_options=assemble_options) + + start_time = time.time() + + converted_docs = doc_converter.convert(input_files) + + success_count = 0 + failure_count = 0 + output_dir.mkdir(parents=True, exist_ok=True) + for doc in converted_docs: + if doc.status != ConversionStatus.SUCCESS: + _log.info(f"Document {doc.input.file} failed to convert.") + failure_count += 1 + continue + + rows = [] + for ( + content_text, + content_md, + content_dt, + page_cells, + page_segments, + page, + ) in generate_multimodal_pages(doc): + + dpi = page._default_image_scale * 72 + + rows.append( + { + "document": doc.input.file.name, + "hash": doc.input.document_hash, + "page_hash": page.page_hash, + "image": { + "width": page.image.width, + "height": page.image.height, + "bytes": page.image.tobytes(), + }, + "cells": page_cells, + "contents": content_text, + "contents_md": content_md, + "contents_dt": content_dt, + "segments": page_segments, + "extra": { + "page_num": page.page_no + 1, + "width_in_points": page.size.width, + "height_in_points": page.size.height, + "dpi": dpi, + }, + } + ) + success_count += 1 + + # Generate one parquet from all documents + df = pd.json_normalize(rows) + now = datetime.datetime.now() + output_filename = output_dir / f"multimodal_{now:%Y-%m-%d_%H%M%S}.parquet" + df.to_parquet(output_filename) + + end_time = time.time() - start_time + + _log.info(f"All documents were converted in {end_time:.2f} seconds.") + + if failure_count > 0: + raise RuntimeError( + f"The example failed converting {failure_count} on {len(input_doc_paths)}." + ) + + # This block demonstrates how the file can be opened with the HF datasets library + # from datasets import Dataset + # from PIL import Image + # multimodal_df = pd.read_parquet(output_filename) + + # # Convert pandas DataFrame to Hugging Face Dataset and load bytes into image + # dataset = Dataset.from_pandas(multimodal_df) + # def transforms(examples): + # examples["image"] = Image.frombytes('RGB', (examples["image.width"], examples["image.height"]), examples["image.bytes"], 'raw') + # return examples + # dataset = dataset.map(transforms) + + +if __name__ == "__main__": + main() diff --git a/docs/examples/export_tables.py b/docs/examples/export_tables.py new file mode 100644 index 00000000..a0c605c1 --- /dev/null +++ b/docs/examples/export_tables.py @@ -0,0 +1,74 @@ +import logging +import time +from pathlib import Path +from typing import Tuple + +import pandas as pd + +from docling.datamodel.base_models import ConversionStatus +from docling.datamodel.document import DocumentConversionInput +from docling.document_converter import DocumentConverter + +_log = logging.getLogger(__name__) + + +def main(): + logging.basicConfig(level=logging.INFO) + + input_doc_paths = [ + Path("./tests/data/2206.01062.pdf"), + ] + output_dir = Path("./scratch") + + input_files = DocumentConversionInput.from_paths(input_doc_paths) + + doc_converter = DocumentConverter() + + start_time = time.time() + + conv_results = doc_converter.convert(input_files) + + success_count = 0 + failure_count = 0 + output_dir.mkdir(parents=True, exist_ok=True) + for conv_res in conv_results: + if conv_res.status != ConversionStatus.SUCCESS: + _log.info(f"Document {conv_res.input.file} failed to convert.") + failure_count += 1 + continue + + doc_filename = conv_res.input.file.stem + + # Export tables + for table_ix, table in enumerate(conv_res.output.tables): + table_df: pd.DataFrame = table.export_to_dataframe() + print(f"## Table {table_ix}") + print(table_df.to_markdown()) + + # Save the table as csv + element_csv_filename = output_dir / f"{doc_filename}-table-{table_ix+1}.csv" + _log.info(f"Saving CSV table to {element_csv_filename}") + table_df.to_csv(element_csv_filename) + + # Save the table as html + element_html_filename = ( + output_dir / f"{doc_filename}-table-{table_ix+1}.html" + ) + _log.info(f"Saving HTML table to {element_html_filename}") + with element_html_filename.open("w") as fp: + fp.write(table.export_to_html()) + + success_count += 1 + + end_time = time.time() - start_time + + _log.info(f"All documents were converted in {end_time:.2f} seconds.") + + if failure_count > 0: + raise RuntimeError( + f"The example failed converting {failure_count} on {len(input_doc_paths)}." + ) + + +if __name__ == "__main__": + main() diff --git a/docs/examples/minimal.py b/docs/examples/minimal.py new file mode 100644 index 00000000..837db718 --- /dev/null +++ b/docs/examples/minimal.py @@ -0,0 +1,6 @@ +from docling.document_converter import DocumentConverter + +source = "https://arxiv.org/pdf/2408.09869" # PDF path or URL +converter = DocumentConverter() +doc = converter.convert_single(source) +print(doc.render_as_markdown()) # output: ## Docling Technical Report [...]" diff --git a/examples/rag_langchain.ipynb b/docs/examples/rag_langchain.ipynb similarity index 99% rename from examples/rag_langchain.ipynb rename to docs/examples/rag_langchain.ipynb index 30e38329..f2464f29 100644 --- a/examples/rag_langchain.ipynb +++ b/docs/examples/rag_langchain.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# RAG with Docling and 🦜🔗 LangChain" + "# RAG with LangChain 🦜🔗" ] }, { diff --git a/examples/rag_llamaindex.ipynb b/docs/examples/rag_llamaindex.ipynb similarity index 98% rename from examples/rag_llamaindex.ipynb rename to docs/examples/rag_llamaindex.ipynb index f5c0e91a..48ade368 100644 --- a/examples/rag_llamaindex.ipynb +++ b/docs/examples/rag_llamaindex.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# RAG with Docling and 🦙 LlamaIndex" + "# RAG with LlamaIndex 🦙" ] }, { @@ -25,9 +25,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "LlamaIndex extensions `DoclingReader` and `DoclingNodeParser` presented in this notebook seamlessly integrate Docling into LlamaIndex, enabling you to:\n", + "This example leverages the official [LlamaIndex Docling extension](../../integrations/llamaindex/).\n", + "\n", + "Presented extensions `DoclingReader` and `DoclingNodeParser` enable you to:\n", "- use PDF documents in your LLM applications with ease and speed, and\n", - "- leverage Docling's rich format for advanced, document-native grounding." + "- harness Docling's rich format for advanced, document-native grounding." ] }, { diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 00000000..6f218e48 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,29 @@ +# Docling + +

+ + Docling + +

+ + +[![arXiv](https://img.shields.io/badge/arXiv-2408.09869-b31b1b.svg)](https://arxiv.org/abs/2408.09869) +[![PyPI version](https://img.shields.io/pypi/v/docling)](https://pypi.org/project/docling/) +![Python](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue) +[![Poetry](https://img.shields.io/endpoint?url=https://python-poetry.org/badge/v0.json)](https://python-poetry.org/) +[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) +[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) +[![Pydantic v2](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/pydantic/pydantic/main/docs/badge/v2.json)](https://pydantic.dev) +[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit) +[![License MIT](https://img.shields.io/github/license/DS4SD/docling)](https://opensource.org/licenses/MIT) + +Docling bundles PDF document conversion to JSON and Markdown in an easy, self-contained package. + +## Features + +* ⚡ Converts any PDF document to JSON or Markdown format, stable and lightning fast +* 📑 Understands detailed page layout, reading order and recovers table structures +* 📝 Extracts metadata from the document, such as title, authors, references and language +* 🔍 Includes OCR support for scanned PDFs +* 🤖 Integrates easily with LLM app / RAG frameworks like LlamaIndex 🦙 & LangChain 🦜🔗 +* 💻 Provides a simple and convenient CLI diff --git a/docs/installation.md b/docs/installation.md new file mode 100644 index 00000000..df18dece --- /dev/null +++ b/docs/installation.md @@ -0,0 +1,100 @@ +To use Docling, simply install `docling` from your Python package manager, e.g. pip: +```bash +pip install docling +``` + +Works on macOS, Linux, and Windows, with support for both x86_64 and arm64 architectures. + +??? "Alternative PyTorch distributions" + + The Docling models depend on the [PyTorch](https://pytorch.org/) library. + Depending on your architecture, you might want to use a different distribution of `torch`. + For example, you might want support for different accelerator or for a cpu-only version. + All the different ways for installing `torch` are listed on their website . + + One common situation is the installation on Linux systems with cpu-only support. + In this case, we suggest the installation of Docling with the following options + + ```bash + # Example for installing on the Linux cpu-only version + pip install docling --extra-index-url https://download.pytorch.org/whl/cpu + ``` + +??? "Alternative OCR engines" + + Docling supports multiple OCR engines for processing scanned documents. The current version provides + the following engines. + + | Engine | Installation | Usage | + | ------ | ------------ | ----- | + | [EasyOCR](https://github.com/JaidedAI/EasyOCR) | Default in Docling or via `pip install easyocr`. | `EasyOcrOptions` | + | Tesseract | System dependency. See description for Tesseract and Tesserocr below. | `TesseractOcrOptions` | + | Tesseract CLI | System dependency. See description below. | `TesseractCliOcrOptions` | + + The Docling `DocumentConverter` allows to choose the OCR engine with the `ocr_options` settings. For example + + ```python + from docling.datamodel.base_models import ConversionStatus, PipelineOptions + from docling.datamodel.pipeline_options import PipelineOptions, EasyOcrOptions, TesseractOcrOptions + from docling.document_converter import DocumentConverter + + pipeline_options = PipelineOptions() + pipeline_options.do_ocr = True + pipeline_options.ocr_options = TesseractOcrOptions() # Use Tesseract + + doc_converter = DocumentConverter( + pipeline_options=pipeline_options, + ) + ``` + +

Tesseract installation

+ + [Tesseract](https://github.com/tesseract-ocr/tesseract) is a popular OCR engine which is available + on most operating systems. For using this engine with Docling, Tesseract must be installed on your + system, using the packaging tool of your choice. Below we provide example commands. + After installing Tesseract you are expected to provide the path to its language files using the + `TESSDATA_PREFIX` environment variable (note that it must terminate with a slash `/`). + + === "macOS (via [Homebrew](https://brew.sh/))" + + ```console + brew install tesseract leptonica pkg-config + TESSDATA_PREFIX=/opt/homebrew/share/tessdata/ + echo "Set TESSDATA_PREFIX=${TESSDATA_PREFIX}" + ``` + + === "Debian-based" + + ```console + apt-get install tesseract-ocr tesseract-ocr-eng libtesseract-dev libleptonica-dev pkg-config + TESSDATA_PREFIX=$(dpkg -L tesseract-ocr-eng | grep tessdata$) + echo "Set TESSDATA_PREFIX=${TESSDATA_PREFIX}" + ``` + + === "RHEL" + + ```console + dnf install tesseract tesseract-devel tesseract-langpack-eng leptonica-devel + TESSDATA_PREFIX=/usr/share/tesseract/tessdata/ + echo "Set TESSDATA_PREFIX=${TESSDATA_PREFIX}" + ``` + +

Linking to Tesseract

+ The most efficient usage of the Tesseract library is via linking. Docling is using + the [Tesserocr](https://github.com/sirfz/tesserocr) package for this. + + If you get into installation issues of Tesserocr, we suggest using the following + installation options: + + ```console + pip uninstall tesserocr + pip install --no-binary :all: tesserocr + ``` + +## Development setup + +To develop Docling features, bugfixes etc., install as follows from your local clone's root dir: + +```bash +poetry install --all-extras +``` diff --git a/docs/integrations/llamaindex.md b/docs/integrations/llamaindex.md new file mode 100644 index 00000000..d3c0f46e --- /dev/null +++ b/docs/integrations/llamaindex.md @@ -0,0 +1,25 @@ +## Get started + +Docling is available as an official LlamaIndex extension! + +To get started, check out the [step-by-step guide in LlamaIndex \[↗\]](https://docs.llamaindex.ai/en/stable/examples/data_connectors/DoclingReaderDemo/). + +## Components + +### Docling Reader + +Reads document files and uses Docling to populate LlamaIndex `Document` objects — either serializing Docling's data model (losslessly, e.g. as JSON) or exporting to a simplified format (lossily, e.g. as Markdown). + +- 💻 [GitHub \[↗\]](https://github.com/run-llama/llama_index/tree/main/llama-index-integrations/readers/llama-index-readers-docling) +- 📖 [API docs \[↗\]](https://docs.llamaindex.ai/en/stable/api_reference/readers/docling/) +- 📦 [PyPI \[↗\]](https://pypi.org/project/llama-index-readers-docling/) +- 🦙 [LlamaHub \[↗\]](https://llamahub.ai/l/readers/llama-index-readers-docling) + +### Docling Node Parser + +Reads LlamaIndex `Document` objects populated in Docling's format by Docling Reader and, using its knowledge of the Docling format, parses them to LlamaIndex `Node` objects for downstream usage in LlamaIndex applications, e.g. as chunks for embedding. + +- 💻 [GitHub \[↗\]](https://github.com/run-llama/llama_index/tree/main/llama-index-integrations/node_parser/llama-index-node-parser-docling) +- 📖 [API docs \[↗\]](https://docs.llamaindex.ai/en/stable/api_reference/node_parser/docling/) +- 📦 [PyPI \[↗\]](https://pypi.org/project/llama-index-node-parser-docling/) +- 🦙 [LlamaHub \[↗\]](https://llamahub.ai/l/node_parser/llama-index-node-parser-docling) diff --git a/docs/overrides/main.html b/docs/overrides/main.html new file mode 100644 index 00000000..a60852d8 --- /dev/null +++ b/docs/overrides/main.html @@ -0,0 +1,7 @@ +{% extends "base.html" %} + +{# +{% block announce %} +

🎉 Docling is now officially supported in LlamaIndex! Check it out!

+{% endblock %} +#} diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css new file mode 100644 index 00000000..5beec977 --- /dev/null +++ b/docs/stylesheets/extra.css @@ -0,0 +1,3 @@ +[data-md-color-scheme="default"] .md-banner a { + color: #5e8bde; +} diff --git a/examples/batch_convert.py b/examples/batch_convert.py deleted file mode 100644 index 2f3f38b1..00000000 --- a/examples/batch_convert.py +++ /dev/null @@ -1,105 +0,0 @@ -import json -import logging -import time -from pathlib import Path -from typing import Iterable - -from docling.datamodel.base_models import ConversionStatus -from docling.datamodel.document import ConversionResult, DocumentConversionInput -from docling.document_converter import DocumentConverter - -_log = logging.getLogger(__name__) - - -def export_documents( - conv_results: Iterable[ConversionResult], - output_dir: Path, -): - output_dir.mkdir(parents=True, exist_ok=True) - - success_count = 0 - failure_count = 0 - partial_success_count = 0 - - for conv_res in conv_results: - if conv_res.status == ConversionStatus.SUCCESS: - success_count += 1 - doc_filename = conv_res.input.file.stem - - # Export Deep Search document JSON format: - with (output_dir / f"{doc_filename}.json").open( - "w", encoding="utf-8" - ) as fp: - fp.write(json.dumps(conv_res.render_as_dict())) - - # Export Text format: - with (output_dir / f"{doc_filename}.txt").open("w", encoding="utf-8") as fp: - fp.write(conv_res.render_as_text()) - - # Export Markdown format: - with (output_dir / f"{doc_filename}.md").open("w", encoding="utf-8") as fp: - fp.write(conv_res.render_as_markdown()) - - # Export Document Tags format: - with (output_dir / f"{doc_filename}.doctags").open( - "w", encoding="utf-8" - ) as fp: - fp.write(conv_res.render_as_doctags()) - - elif conv_res.status == ConversionStatus.PARTIAL_SUCCESS: - _log.info( - f"Document {conv_res.input.file} was partially converted with the following errors:" - ) - for item in conv_res.errors: - _log.info(f"\t{item.error_message}") - partial_success_count += 1 - else: - _log.info(f"Document {conv_res.input.file} failed to convert.") - failure_count += 1 - - _log.info( - f"Processed {success_count + partial_success_count + failure_count} docs, " - f"of which {failure_count} failed " - f"and {partial_success_count} were partially converted." - ) - return success_count, partial_success_count, failure_count - - -def main(): - logging.basicConfig(level=logging.INFO) - - input_doc_paths = [ - Path("./tests/data/2206.01062.pdf"), - Path("./tests/data/2203.01017v2.pdf"), - Path("./tests/data/2305.03393v1.pdf"), - Path("./tests/data/redp5110.pdf"), - Path("./tests/data/redp5695.pdf"), - ] - - # buf = BytesIO(Path("./test/data/2206.01062.pdf").open("rb").read()) - # docs = [DocumentStream(filename="my_doc.pdf", stream=buf)] - # input = DocumentConversionInput.from_streams(docs) - - doc_converter = DocumentConverter() - - input = DocumentConversionInput.from_paths(input_doc_paths) - - start_time = time.time() - - conv_results = doc_converter.convert(input) - success_count, partial_success_count, failure_count = export_documents( - conv_results, output_dir=Path("./scratch") - ) - - end_time = time.time() - start_time - - _log.info(f"All documents were converted in {end_time:.2f} seconds.") - - if failure_count > 0: - raise RuntimeError( - f"The example failed converting {failure_count} on {len(input_doc_paths)}." - ) - - -if __name__ == "__main__": - main() diff --git a/examples/batch_convert.py b/examples/batch_convert.py new file mode 120000 index 00000000..5117377f --- /dev/null +++ b/examples/batch_convert.py @@ -0,0 +1 @@ +../docs/examples/batch_convert.py \ No newline at end of file diff --git a/examples/custom_convert.py b/examples/custom_convert.py deleted file mode 100644 index e386bb3a..00000000 --- a/examples/custom_convert.py +++ /dev/null @@ -1,175 +0,0 @@ -import json -import logging -import time -from pathlib import Path -from typing import Iterable - -from docling.backend.docling_parse_backend import DoclingParseDocumentBackend -from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend -from docling.datamodel.base_models import ConversionStatus, PipelineOptions -from docling.datamodel.document import ConversionResult, DocumentConversionInput -from docling.datamodel.pipeline_options import ( - TesseractCliOcrOptions, - TesseractOcrOptions, -) -from docling.document_converter import DocumentConverter - -_log = logging.getLogger(__name__) - - -def export_documents( - conv_results: Iterable[ConversionResult], - output_dir: Path, -): - output_dir.mkdir(parents=True, exist_ok=True) - - success_count = 0 - failure_count = 0 - - for conv_res in conv_results: - if conv_res.status == ConversionStatus.SUCCESS: - success_count += 1 - doc_filename = conv_res.input.file.stem - - # Export Deep Search document JSON format: - with (output_dir / f"{doc_filename}.json").open( - "w", encoding="utf-8" - ) as fp: - fp.write(json.dumps(conv_res.render_as_dict())) - - # Export Text format: - with (output_dir / f"{doc_filename}.txt").open("w", encoding="utf-8") as fp: - fp.write(conv_res.render_as_text()) - - # Export Markdown format: - with (output_dir / f"{doc_filename}.md").open("w", encoding="utf-8") as fp: - fp.write(conv_res.render_as_markdown()) - - # Export Document Tags format: - with (output_dir / f"{doc_filename}.doctags").open( - "w", encoding="utf-8" - ) as fp: - fp.write(conv_res.render_as_doctags()) - - else: - _log.info(f"Document {conv_res.input.file} failed to convert.") - failure_count += 1 - - _log.info( - f"Processed {success_count + failure_count} docs, of which {failure_count} failed" - ) - - return success_count, failure_count - - -def main(): - logging.basicConfig(level=logging.INFO) - - input_doc_paths = [ - Path("./tests/data/2206.01062.pdf"), - ] - - ########################################################################### - - # The following sections contain a combination of PipelineOptions - # and PDF Backends for various configurations. - # Uncomment one section at the time to see the differences in the output. - - # PyPdfium without EasyOCR - # -------------------- - # pipeline_options = PipelineOptions() - # pipeline_options.do_ocr=False - # pipeline_options.do_table_structure=True - # pipeline_options.table_structure_options.do_cell_matching = False - - # doc_converter = DocumentConverter( - # pipeline_options=pipeline_options, - # pdf_backend=PyPdfiumDocumentBackend, - # ) - - # PyPdfium with EasyOCR - # ----------------- - # pipeline_options = PipelineOptions() - # pipeline_options.do_ocr=True - # pipeline_options.do_table_structure=True - # pipeline_options.table_structure_options.do_cell_matching = True - - # doc_converter = DocumentConverter( - # pipeline_options=pipeline_options, - # pdf_backend=PyPdfiumDocumentBackend, - # ) - - # Docling Parse without EasyOCR - # ------------------------- - pipeline_options = PipelineOptions() - pipeline_options.do_ocr = False - pipeline_options.do_table_structure = True - pipeline_options.table_structure_options.do_cell_matching = True - - doc_converter = DocumentConverter( - pipeline_options=pipeline_options, - pdf_backend=DoclingParseDocumentBackend, - ) - - # Docling Parse with EasyOCR - # ---------------------- - # pipeline_options = PipelineOptions() - # pipeline_options.do_ocr=True - # pipeline_options.do_table_structure=True - # pipeline_options.table_structure_options.do_cell_matching = True - - # doc_converter = DocumentConverter( - # pipeline_options=pipeline_options, - # pdf_backend=DoclingParseDocumentBackend, - # ) - - # Docling Parse with Tesseract - # ---------------------- - # pipeline_options = PipelineOptions() - # pipeline_options.do_ocr = True - # pipeline_options.do_table_structure = True - # pipeline_options.table_structure_options.do_cell_matching = True - # pipeline_options.ocr_options = TesseractOcrOptions() - - # doc_converter = DocumentConverter( - # pipeline_options=pipeline_options, - # pdf_backend=DoclingParseDocumentBackend, - # ) - - # Docling Parse with Tesseract CLI - # ---------------------- - # pipeline_options = PipelineOptions() - # pipeline_options.do_ocr = True - # pipeline_options.do_table_structure = True - # pipeline_options.table_structure_options.do_cell_matching = True - # pipeline_options.ocr_options = TesseractCliOcrOptions() - - # doc_converter = DocumentConverter( - # pipeline_options=pipeline_options, - # pdf_backend=DoclingParseDocumentBackend, - # ) - - ########################################################################### - - # Define input files - input = DocumentConversionInput.from_paths(input_doc_paths) - - start_time = time.time() - - conv_results = doc_converter.convert(input) - success_count, failure_count = export_documents( - conv_results, output_dir=Path("./scratch") - ) - - end_time = time.time() - start_time - - _log.info(f"All documents were converted in {end_time:.2f} seconds.") - - if failure_count > 0: - raise RuntimeError( - f"The example failed converting {failure_count} on {len(input_doc_paths)}." - ) - - -if __name__ == "__main__": - main() diff --git a/examples/custom_convert.py b/examples/custom_convert.py new file mode 120000 index 00000000..dc4e853e --- /dev/null +++ b/examples/custom_convert.py @@ -0,0 +1 @@ +../docs/examples/custom_convert.py \ No newline at end of file diff --git a/examples/export_figures.py b/examples/export_figures.py deleted file mode 100644 index bdffbec1..00000000 --- a/examples/export_figures.py +++ /dev/null @@ -1,85 +0,0 @@ -import logging -import time -from pathlib import Path -from typing import Tuple - -from docling.datamodel.base_models import ( - AssembleOptions, - ConversionStatus, - FigureElement, - PageElement, - TableElement, -) -from docling.datamodel.document import DocumentConversionInput -from docling.document_converter import DocumentConverter - -_log = logging.getLogger(__name__) - -IMAGE_RESOLUTION_SCALE = 2.0 - - -def main(): - logging.basicConfig(level=logging.INFO) - - input_doc_paths = [ - Path("./tests/data/2206.01062.pdf"), - ] - output_dir = Path("./scratch") - - input_files = DocumentConversionInput.from_paths(input_doc_paths) - - # Important: For operating with page images, we must keep them, otherwise the DocumentConverter - # will destroy them for cleaning up memory. - # This is done by setting AssembleOptions.images_scale, which also defines the scale of images. - # scale=1 correspond of a standard 72 DPI image - assemble_options = AssembleOptions() - assemble_options.images_scale = IMAGE_RESOLUTION_SCALE - - doc_converter = DocumentConverter(assemble_options=assemble_options) - - start_time = time.time() - - conv_results = doc_converter.convert(input_files) - - success_count = 0 - failure_count = 0 - output_dir.mkdir(parents=True, exist_ok=True) - for conv_res in conv_results: - if conv_res.status != ConversionStatus.SUCCESS: - _log.info(f"Document {conv_res.input.file} failed to convert.") - failure_count += 1 - continue - - doc_filename = conv_res.input.file.stem - - # Export page images - for page in conv_res.pages: - page_no = page.page_no + 1 - page_image_filename = output_dir / f"{doc_filename}-{page_no}.png" - with page_image_filename.open("wb") as fp: - page.image.save(fp, format="PNG") - - # Export figures and tables - for element, image in conv_res.render_element_images( - element_types=(FigureElement, TableElement) - ): - element_image_filename = ( - output_dir / f"{doc_filename}-element-{element.id}.png" - ) - with element_image_filename.open("wb") as fp: - image.save(fp, "PNG") - - success_count += 1 - - end_time = time.time() - start_time - - _log.info(f"All documents were converted in {end_time:.2f} seconds.") - - if failure_count > 0: - raise RuntimeError( - f"The example failed converting {failure_count} on {len(input_doc_paths)}." - ) - - -if __name__ == "__main__": - main() diff --git a/examples/export_figures.py b/examples/export_figures.py new file mode 120000 index 00000000..831087f5 --- /dev/null +++ b/examples/export_figures.py @@ -0,0 +1 @@ +../docs/examples/export_figures.py \ No newline at end of file diff --git a/examples/export_multimodal.py b/examples/export_multimodal.py deleted file mode 100644 index 7c016b19..00000000 --- a/examples/export_multimodal.py +++ /dev/null @@ -1,116 +0,0 @@ -import datetime -import logging -import time -from pathlib import Path - -import pandas as pd - -from docling.datamodel.base_models import AssembleOptions, ConversionStatus -from docling.datamodel.document import DocumentConversionInput -from docling.document_converter import DocumentConverter -from docling.utils.export import generate_multimodal_pages - -_log = logging.getLogger(__name__) - -IMAGE_RESOLUTION_SCALE = 2.0 - - -def main(): - logging.basicConfig(level=logging.INFO) - - input_doc_paths = [ - Path("./tests/data/2206.01062.pdf"), - ] - output_dir = Path("./scratch") - - input_files = DocumentConversionInput.from_paths(input_doc_paths) - - # Important: For operating with page images, we must keep them, otherwise the DocumentConverter - # will destroy them for cleaning up memory. - # This is done by setting AssembleOptions.images_scale, which also defines the scale of images. - # scale=1 correspond of a standard 72 DPI image - assemble_options = AssembleOptions() - assemble_options.images_scale = IMAGE_RESOLUTION_SCALE - - doc_converter = DocumentConverter(assemble_options=assemble_options) - - start_time = time.time() - - converted_docs = doc_converter.convert(input_files) - - success_count = 0 - failure_count = 0 - output_dir.mkdir(parents=True, exist_ok=True) - for doc in converted_docs: - if doc.status != ConversionStatus.SUCCESS: - _log.info(f"Document {doc.input.file} failed to convert.") - failure_count += 1 - continue - - rows = [] - for ( - content_text, - content_md, - content_dt, - page_cells, - page_segments, - page, - ) in generate_multimodal_pages(doc): - - dpi = page._default_image_scale * 72 - - rows.append( - { - "document": doc.input.file.name, - "hash": doc.input.document_hash, - "page_hash": page.page_hash, - "image": { - "width": page.image.width, - "height": page.image.height, - "bytes": page.image.tobytes(), - }, - "cells": page_cells, - "contents": content_text, - "contents_md": content_md, - "contents_dt": content_dt, - "segments": page_segments, - "extra": { - "page_num": page.page_no + 1, - "width_in_points": page.size.width, - "height_in_points": page.size.height, - "dpi": dpi, - }, - } - ) - success_count += 1 - - # Generate one parquet from all documents - df = pd.json_normalize(rows) - now = datetime.datetime.now() - output_filename = output_dir / f"multimodal_{now:%Y-%m-%d_%H%M%S}.parquet" - df.to_parquet(output_filename) - - end_time = time.time() - start_time - - _log.info(f"All documents were converted in {end_time:.2f} seconds.") - - if failure_count > 0: - raise RuntimeError( - f"The example failed converting {failure_count} on {len(input_doc_paths)}." - ) - - # This block demonstrates how the file can be opened with the HF datasets library - # from datasets import Dataset - # from PIL import Image - # multimodal_df = pd.read_parquet(output_filename) - - # # Convert pandas DataFrame to Hugging Face Dataset and load bytes into image - # dataset = Dataset.from_pandas(multimodal_df) - # def transforms(examples): - # examples["image"] = Image.frombytes('RGB', (examples["image.width"], examples["image.height"]), examples["image.bytes"], 'raw') - # return examples - # dataset = dataset.map(transforms) - - -if __name__ == "__main__": - main() diff --git a/examples/export_multimodal.py b/examples/export_multimodal.py new file mode 120000 index 00000000..96ea158a --- /dev/null +++ b/examples/export_multimodal.py @@ -0,0 +1 @@ +../docs/examples/export_multimodal.py \ No newline at end of file diff --git a/examples/export_tables.py b/examples/export_tables.py deleted file mode 100644 index a0c605c1..00000000 --- a/examples/export_tables.py +++ /dev/null @@ -1,74 +0,0 @@ -import logging -import time -from pathlib import Path -from typing import Tuple - -import pandas as pd - -from docling.datamodel.base_models import ConversionStatus -from docling.datamodel.document import DocumentConversionInput -from docling.document_converter import DocumentConverter - -_log = logging.getLogger(__name__) - - -def main(): - logging.basicConfig(level=logging.INFO) - - input_doc_paths = [ - Path("./tests/data/2206.01062.pdf"), - ] - output_dir = Path("./scratch") - - input_files = DocumentConversionInput.from_paths(input_doc_paths) - - doc_converter = DocumentConverter() - - start_time = time.time() - - conv_results = doc_converter.convert(input_files) - - success_count = 0 - failure_count = 0 - output_dir.mkdir(parents=True, exist_ok=True) - for conv_res in conv_results: - if conv_res.status != ConversionStatus.SUCCESS: - _log.info(f"Document {conv_res.input.file} failed to convert.") - failure_count += 1 - continue - - doc_filename = conv_res.input.file.stem - - # Export tables - for table_ix, table in enumerate(conv_res.output.tables): - table_df: pd.DataFrame = table.export_to_dataframe() - print(f"## Table {table_ix}") - print(table_df.to_markdown()) - - # Save the table as csv - element_csv_filename = output_dir / f"{doc_filename}-table-{table_ix+1}.csv" - _log.info(f"Saving CSV table to {element_csv_filename}") - table_df.to_csv(element_csv_filename) - - # Save the table as html - element_html_filename = ( - output_dir / f"{doc_filename}-table-{table_ix+1}.html" - ) - _log.info(f"Saving HTML table to {element_html_filename}") - with element_html_filename.open("w") as fp: - fp.write(table.export_to_html()) - - success_count += 1 - - end_time = time.time() - start_time - - _log.info(f"All documents were converted in {end_time:.2f} seconds.") - - if failure_count > 0: - raise RuntimeError( - f"The example failed converting {failure_count} on {len(input_doc_paths)}." - ) - - -if __name__ == "__main__": - main() diff --git a/examples/export_tables.py b/examples/export_tables.py new file mode 120000 index 00000000..c6842a50 --- /dev/null +++ b/examples/export_tables.py @@ -0,0 +1 @@ +../docs/examples/export_tables.py \ No newline at end of file diff --git a/examples/minimal.py b/examples/minimal.py deleted file mode 100644 index 837db718..00000000 --- a/examples/minimal.py +++ /dev/null @@ -1,6 +0,0 @@ -from docling.document_converter import DocumentConverter - -source = "https://arxiv.org/pdf/2408.09869" # PDF path or URL -converter = DocumentConverter() -doc = converter.convert_single(source) -print(doc.render_as_markdown()) # output: ## Docling Technical Report [...]" diff --git a/examples/minimal.py b/examples/minimal.py new file mode 120000 index 00000000..0964db6d --- /dev/null +++ b/examples/minimal.py @@ -0,0 +1 @@ +../docs/examples/minimal.py \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 00000000..2deb6463 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,97 @@ +site_name: Docling +site_url: https://ds4sd.github.io/docling/ +repo_name: DS4SD/docling +repo_url: https://github.com/DS4SD/docling + +theme: + name: material + custom_dir: docs/overrides + palette: + # Palette toggle for automatic mode + - media: "(prefers-color-scheme)" + scheme: default + primary: black + toggle: + icon: material/brightness-auto + name: Switch to light mode + + # Palette toggle for light mode + - media: "(prefers-color-scheme: light)" + scheme: default + primary: black + toggle: + icon: material/brightness-7 + name: Switch to dark mode + + # Palette toggle for dark mode + - media: "(prefers-color-scheme: dark)" + scheme: slate + primary: black + toggle: + icon: material/brightness-4 + name: Switch to system preference + + logo: assets/logo.png + favicon: assets/logo.png + features: + - content.tabs.link + - content.code.annotate + - content.code.copy + - announce.dismiss + - navigation.tabs + # - navigation.indexes # <= if set, each "section" can have its own page, if index.md is used + - navigation.instant + - navigation.instant.prefetch + # - navigation.instant.preview + - navigation.instant.progress + - navigation.path + - navigation.sections # <= + - navigation.top + - navigation.tracking + - search.suggest + - toc.follow +nav: + - Get started: + - Home: index.md + - Installation: installation.md + # - Docling v2: v2.md + # - Concepts: + # - Docling Document: concepts/document.md + # - Chunking: concepts/chunking.md + - Examples: + - Conversion: + - "Simple conversion": examples/minimal.py + - "Custom conversion": examples/custom_convert.py + - "Batch conversion": examples/batch_convert.py + - "Figure export": examples/export_figures.py + - "Table export": examples/export_tables.py + - "Multimodal export": examples/export_multimodal.py + - RAG / QA: + - "RAG with LlamaIndex 🦙": examples/rag_llamaindex.ipynb + - "RAG with LangChain 🦜🔗": examples/rag_langchain.ipynb + # - Chunking: + # - Chunking: examples/chunking.md + # - CLI: + # - CLI: examples/cli.md + - Integrations: + - "LlamaIndex 🦙 extension": integrations/llamaindex.md + # - "LangChain 🦜🔗 extension": integrations/langchain.md + # - API reference: + # - API reference: api_reference/index.md + +markdown_extensions: + - pymdownx.superfences + - pymdownx.tabbed: + alternate_style: true + slugify: !!python/object/apply:pymdownx.slugs.slugify + kwds: + case: lower + - admonition + - pymdownx.details + - attr_list +plugins: + - search + - mkdocs-jupyter + +extra_css: + - stylesheets/extra.css diff --git a/poetry.lock b/poetry.lock index 307927dd..052e8743 100644 --- a/poetry.lock +++ b/poetry.lock @@ -263,6 +263,20 @@ files = [ pycodestyle = ">=2.11.0" tomli = {version = "*", markers = "python_version < \"3.11\""} +[[package]] +name = "babel" +version = "2.16.0" +description = "Internationalization utilities" +optional = false +python-versions = ">=3.8" +files = [ + 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+[package.extras] +css = ["tinycss2 (>=1.1.0,<1.3)"] + [[package]] name = "certifi" version = "2024.8.30" @@ -931,6 +963,17 @@ tqdm = ">=4.64.0,<5.0.0" [package.extras] toolkit = ["deepsearch-toolkit (>=0.31.0)"] +[[package]] +name = "defusedxml" +version = "0.7.1" +description = "XML bomb protection for Python stdlib modules" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +files = [ + {file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"}, + {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"}, +] + [[package]] name = "deprecated" version = "1.2.14" @@ -1185,6 +1228,20 @@ files = [ [package.extras] tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"] +[[package]] +name = "fastjsonschema" +version = "2.20.0" +description = "Fastest Python implementation of JSON schema" +optional = false +python-versions = "*" +files = [ + {file = "fastjsonschema-2.20.0-py3-none-any.whl", hash = "sha256:5875f0b0fa7a0043a91e93a9b8f793bcbbba9691e7fd83dca95c28ba26d21f0a"}, + {file = "fastjsonschema-2.20.0.tar.gz", hash = "sha256:3d48fc5300ee96f5d116f10fe6f28d938e6008f59a6a025c2649475b87f76a23"}, +] + +[package.extras] +devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"] + [[package]] name = "filelock" version = "3.16.1" @@ -1444,6 +1501,23 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe, test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"] tqdm = ["tqdm"] +[[package]] +name = "ghp-import" +version = "2.1.0" +description = "Copy your docs directly to the gh-pages branch." +optional = false +python-versions = "*" +files = [ + {file = "ghp-import-2.1.0.tar.gz", hash = "sha256:9c535c4c61193c2df8871222567d7fd7e5014d835f97dc7b7439069e2413d343"}, + {file = "ghp_import-2.1.0-py3-none-any.whl", hash = "sha256:8337dd7b50877f163d4c0289bc1f1c7f127550241988d568c1db512c4324a619"}, +] + +[package.dependencies] +python-dateutil = ">=2.8.1" + +[package.extras] +dev = ["flake8", "markdown", "twine", "wheel"] + [[package]] name = "gitdb" version = "4.0.11" @@ -2214,6 +2288,17 @@ traitlets = ">=5.3" docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"] test = ["ipykernel", 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+[[package]] +name = "webencodings" +version = "0.5.1" +description = "Character encoding aliases for legacy web content" +optional = false +python-versions = "*" +files = [ + {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"}, + {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"}, +] + [[package]] name = "wheel" version = "0.44.0" @@ -7462,4 +7941,4 @@ tesserocr = ["tesserocr"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "536b2f199fe70180aa31e55e7ad47a75a0b64cd20bbe96caec294037966c7b00" +content-hash = "cae7819c1a144a8aa2b700d0399d7e9d78b55b3c743cfb0b118f4bd0baa2d34e" diff --git a/pyproject.toml b/pyproject.toml index 015cb384..1d6c4ae3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -72,6 +72,8 @@ pandas-stubs = "^2.1.4.231227" ipykernel = "^6.29.5" ipywidgets = "^8.1.5" nbqa = "^1.9.0" +mkdocs-material = "^9.5.40" +mkdocs-jupyter = "^0.25.0" [tool.poetry.group.examples.dependencies] datasets = "^2.21.0" From 27f4ed36207fcd64ccd01a31af43df86ccac3a2a Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Tue, 15 Oct 2024 14:58:00 +0200 Subject: [PATCH 05/10] Enable mypy and fix many reported errors Signed-off-by: Christoph Auer --- .pre-commit-config.yaml | 12 +- docling/backend/docling_parse_v2_backend.py | 5 +- docling/backend/html_backend.py | 21 +- docling/backend/mspowerpoint_backend.py | 12 +- docling/backend/msword_backend.py | 27 +- docling/backend/pdf_backend.py | 2 +- docling/backend/pypdfium2_backend.py | 5 +- docling/cli/main.py | 5 +- docling/datamodel/base_models.py | 3 +- docling/datamodel/document.py | 64 +-- docling/document_converter.py | 11 +- docling/models/base_ocr_model.py | 10 +- docling/models/ds_glm_model.py | 27 +- docling/models/easyocr_model.py | 2 + docling/models/layout_model.py | 45 +- docling/models/page_assemble_model.py | 2 + docling/models/page_preprocessing_model.py | 4 +- docling/models/table_structure_model.py | 32 +- docling/models/tesseract_ocr_cli_model.py | 10 +- docling/models/tesseract_ocr_model.py | 3 + docling/pipeline/base_pipeline.py | 2 +- docling/pipeline/standard_pdf_pipeline.py | 7 +- examples/rag_langchain.ipynb | 369 ----------------- examples/rag_llamaindex.ipynb | 434 -------------------- pyproject.toml | 10 + tests/test_backend_docling_parse_v2.py | 1 - 26 files changed, 205 insertions(+), 920 deletions(-) delete mode 100644 examples/rag_langchain.ipynb delete mode 100644 examples/rag_llamaindex.ipynb diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index c4baa518..6ab5c1aa 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -20,12 +20,12 @@ repos: # pass_filenames: false # language: system # files: '\.py$' -# - id: mypy -# name: MyPy -# entry: poetry run mypy docling -# pass_filenames: false -# language: system -# files: '\.py$' + - id: mypy + name: MyPy + entry: poetry run mypy docling + pass_filenames: false + language: system + files: '\.py$' - id: nbqa_black name: nbQA Black entry: poetry run nbqa black examples diff --git a/docling/backend/docling_parse_v2_backend.py b/docling/backend/docling_parse_v2_backend.py index 239ea9af..bfc68f9b 100644 --- a/docling/backend/docling_parse_v2_backend.py +++ b/docling/backend/docling_parse_v2_backend.py @@ -2,7 +2,7 @@ import logging import random from io import BytesIO from pathlib import Path -from typing import Iterable, List, Optional, Union +from typing import TYPE_CHECKING, Iterable, List, Optional, Union import pypdfium2 as pdfium from docling_core.types.doc import BoundingBox, CoordOrigin @@ -13,6 +13,9 @@ from pypdfium2 import PdfPage from docling.backend.pdf_backend import PdfDocumentBackend, PdfPageBackend from docling.datamodel.base_models import Cell, Size +if TYPE_CHECKING: + from docling.datamodel.document import InputDocument + _log = logging.getLogger(__name__) diff --git a/docling/backend/html_backend.py b/docling/backend/html_backend.py index b9f7e630..7878d64f 100644 --- a/docling/backend/html_backend.py +++ b/docling/backend/html_backend.py @@ -30,10 +30,10 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend): # Initialise the parents for the hierarchy self.max_levels = 10 self.level = 0 - self.parents = {} + self.parents = {} # type: ignore for i in range(0, self.max_levels): self.parents[i] = None - self.labels = {} + self.labels = {} # type: ignore try: if isinstance(self.path_or_stream, BytesIO): @@ -49,8 +49,9 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend): ) from e def is_valid(self) -> bool: - return True + return self.soup is not None + @classmethod def supports_pagination(cls) -> bool: return False @@ -68,11 +69,17 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend): # access self.path_or_stream to load stuff doc = DoclingDocument(description=DescriptionItem(), name="dummy") _log.debug("Trying to convert HTML...") - # Replace
tags with newline characters - for br in self.soup.body.find_all("br"): - br.replace_with("\n") - doc = self.walk(self.soup.body, doc) + if self.is_valid(): + assert self.soup is not None + # Replace
tags with newline characters + for br in self.soup.body.find_all("br"): + br.replace_with("\n") + doc = self.walk(self.soup.body, doc) + else: + raise RuntimeError( + f"Cannot convert doc with {self.document_hash} because the backend failed to init." + ) return doc def walk(self, element, doc): diff --git a/docling/backend/mspowerpoint_backend.py b/docling/backend/mspowerpoint_backend.py index bdf73c99..2455389a 100644 --- a/docling/backend/mspowerpoint_backend.py +++ b/docling/backend/mspowerpoint_backend.py @@ -42,7 +42,11 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB self.pptx_obj = None self.valid = False try: - self.pptx_obj = Presentation(self.path_or_stream) + if isinstance(self.path_or_stream, BytesIO): + self.pptx_obj = Presentation(self.path_or_stream) + elif isinstance(self.path_or_stream, Path): + self.pptx_obj = Presentation(str(self.path_or_stream)) + self.valid = True except Exception as e: raise RuntimeError( @@ -53,6 +57,7 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB def page_count(self) -> int: if self.is_valid(): + assert self.pptx_obj is not None return len(self.pptx_obj.slides) else: return 0 @@ -60,6 +65,7 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB def is_valid(self) -> bool: return self.valid + @classmethod def supports_pagination(cls) -> bool: return True # True? if so, how to handle pages... @@ -311,10 +317,10 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB slide_width = pptx_obj.slide_width slide_height = pptx_obj.slide_height - text_content = [] + text_content = [] # type: ignore max_levels = 10 - parents = {} + parents = {} # type: ignore for i in range(0, max_levels): parents[i] = None diff --git a/docling/backend/msword_backend.py b/docling/backend/msword_backend.py index a098db51..7bf0946b 100644 --- a/docling/backend/msword_backend.py +++ b/docling/backend/msword_backend.py @@ -39,7 +39,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend): # Initialise the parents for the hierarchy self.max_levels = 10 self.level_at_new_list = None - self.parents = {} + self.parents = {} # type: ignore for i in range(-1, self.max_levels): self.parents[i] = None @@ -54,16 +54,21 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend): self.docx_obj = None try: - self.docx_obj = docx.Document(self.path_or_stream) + if isinstance(self.path_or_stream, BytesIO): + self.docx_obj = docx.Document(self.path_or_stream) + elif isinstance(self.path_or_stream, Path): + self.docx_obj = docx.Document(str(self.path_or_stream)) + self.valid = True except Exception as e: raise RuntimeError( - f"MsPowerpointDocumentBackend could not load document with hash {document_hash}" + f"MsPowerpointDocumentBackend could not load document with hash {self.document_hash}" ) from e def is_valid(self) -> bool: - return True + return self.valid + @classmethod def supports_pagination(cls) -> bool: return False @@ -80,10 +85,14 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend): def convert(self) -> DoclingDocument: # Parses the DOCX into a structured document model. doc = DoclingDocument(description=DescriptionItem(), name="dummy") - - # self.initialise() - doc = self.walk_linear(self.docx_obj.element.body, self.docx_obj, doc) - return doc + if self.is_valid(): + assert self.docx_obj is not None + doc = self.walk_linear(self.docx_obj.element.body, self.docx_obj, doc) + return doc + else: + raise RuntimeError( + f"Cannot convert doc with {self.document_hash} because the backend failed to init." + ) def update_history(self, name, level, numid, ilevel): self.history["names"].append(name) @@ -307,7 +316,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend): ): level = self.get_level() if self.prev_numid() is None: # Open new list - self.level_at_new_list = level + self.level_at_new_list = level # type: ignore self.parents[level] = doc.add_group( label=GroupLabel.LIST, name="list", parent=self.parents[level - 1] diff --git a/docling/backend/pdf_backend.py b/docling/backend/pdf_backend.py index 0bac725b..cd7a0815 100644 --- a/docling/backend/pdf_backend.py +++ b/docling/backend/pdf_backend.py @@ -1,9 +1,9 @@ from abc import ABC, abstractmethod from io import BytesIO +from pathlib import Path from typing import Iterable, Optional, Set, Union from docling_core.types.doc import BoundingBox, Size -from docling_core.types.legacy_doc.doc_ocr import Path from PIL import Image from docling.backend.abstract_backend import PaginatedDocumentBackend diff --git a/docling/backend/pypdfium2_backend.py b/docling/backend/pypdfium2_backend.py index 5631a50d..4ab100a3 100644 --- a/docling/backend/pypdfium2_backend.py +++ b/docling/backend/pypdfium2_backend.py @@ -2,7 +2,7 @@ import logging import random from io import BytesIO from pathlib import Path -from typing import Iterable, List, Optional, Union +from typing import TYPE_CHECKING, Iterable, List, Optional, Union import pypdfium2 as pdfium import pypdfium2.raw as pdfium_c @@ -14,6 +14,9 @@ from pypdfium2._helpers.misc import PdfiumError from docling.backend.pdf_backend import PdfDocumentBackend, PdfPageBackend from docling.datamodel.base_models import Cell +if TYPE_CHECKING: + from docling.datamodel.document import InputDocument + _log = logging.getLogger(__name__) diff --git a/docling/cli/main.py b/docling/cli/main.py index a1f25bb0..6610cef2 100644 --- a/docling/cli/main.py +++ b/docling/cli/main.py @@ -21,6 +21,7 @@ from docling.datamodel.base_models import ( from docling.datamodel.document import ConversionResult from docling.datamodel.pipeline_options import ( EasyOcrOptions, + OcrOptions, PdfPipelineOptions, TesseractCliOcrOptions, TesseractOcrOptions, @@ -179,7 +180,7 @@ def convert( raise typer.Abort() elif source.is_dir(): for fmt in from_formats: - for ext in FormatToExtensions.get(fmt): + for ext in FormatToExtensions[fmt]: input_doc_paths.extend(list(source.glob(f"**/*.{ext}"))) input_doc_paths.extend(list(source.glob(f"**/*.{ext.upper()}"))) else: @@ -195,7 +196,7 @@ def convert( match ocr_engine: case OcrEngine.EASYOCR: - ocr_options = EasyOcrOptions() + ocr_options: OcrOptions = EasyOcrOptions() case OcrEngine.TESSERACT_CLI: ocr_options = TesseractCliOcrOptions() case OcrEngine.TESSERACT: diff --git a/docling/datamodel/base_models.py b/docling/datamodel/base_models.py index c915e419..3a893fa1 100644 --- a/docling/datamodel/base_models.py +++ b/docling/datamodel/base_models.py @@ -126,7 +126,8 @@ class TableStructurePrediction(BaseModel): table_map: Dict[int, Table] = {} -class TextElement(BasePageElement): ... +class TextElement(BasePageElement): + text: str class FigureElement(BasePageElement): diff --git a/docling/datamodel/document.py b/docling/datamodel/document.py index b8bab5fa..bcc0254e 100644 --- a/docling/datamodel/document.py +++ b/docling/datamodel/document.py @@ -3,7 +3,7 @@ import re from enum import Enum from io import BytesIO from pathlib import Path, PurePath -from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Tuple, Union +from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Tuple, Type, Union import filetype from docling_core.types import BaseText @@ -29,7 +29,10 @@ from docling_core.utils.file import resolve_file_source from pydantic import BaseModel from typing_extensions import deprecated -from docling.backend.abstract_backend import AbstractDocumentBackend +from docling.backend.abstract_backend import ( + AbstractDocumentBackend, + PaginatedDocumentBackend, +) from docling.datamodel.base_models import ( AssembledUnit, ConversionStatus, @@ -70,41 +73,34 @@ layout_label_to_ds_type = { DocItemLabel.PARAGRAPH: "paragraph", } -_EMPTY_LEGACY_DOC = DsDocument( - _name="", - description=DsDocumentDescription(logs=[]), - file_info=DsFileInfoObject( - filename="", - document_hash="", - ), -) - _EMPTY_DOCLING_DOC = DoclingDocument( description=DescriptionItem(), name="dummy" ) # TODO: Stub class InputDocument(BaseModel): - file: PurePath = None - document_hash: Optional[str] = None + file: PurePath + document_hash: str # = None valid: bool = True limits: DocumentLimits = DocumentLimits() - format: Optional[InputFormat] = None + format: InputFormat # = None filesize: Optional[int] = None page_count: int = 0 - _backend: AbstractDocumentBackend = None # Internal PDF backend used + _backend: AbstractDocumentBackend # Internal PDF backend used def __init__( self, path_or_stream: Union[BytesIO, Path], format: InputFormat, - backend: AbstractDocumentBackend, + backend: Type[AbstractDocumentBackend], filename: Optional[str] = None, limits: Optional[DocumentLimits] = None, ): - super().__init__() + super().__init__( + file="", document_hash="", format=InputFormat.PDF + ) # initialize with dummy values self.limits = limits or DocumentLimits() self.format = format @@ -120,6 +116,9 @@ class InputDocument(BaseModel): self._init_doc(backend, path_or_stream) elif isinstance(path_or_stream, BytesIO): + assert ( + filename is not None + ), "Can't construct InputDocument from stream without providing filename arg." self.file = PurePath(filename) self.filesize = path_or_stream.getbuffer().nbytes @@ -128,10 +127,16 @@ class InputDocument(BaseModel): else: self.document_hash = create_file_hash(path_or_stream) self._init_doc(backend, path_or_stream) + else: + raise RuntimeError( + f"Unexpected type path_or_stream: {type(path_or_stream)}" + ) # For paginated backends, check if the maximum page count is exceeded. if self.valid and self._backend.is_valid(): - if self._backend.supports_pagination(): + if self._backend.supports_pagination() and isinstance( + self._backend, PaginatedDocumentBackend + ): self.page_count = self._backend.page_count() if not self.page_count <= self.limits.max_num_pages: self.valid = False @@ -150,7 +155,7 @@ class InputDocument(BaseModel): def _init_doc( self, - backend: AbstractDocumentBackend, + backend: Type[AbstractDocumentBackend], path_or_stream: Union[BytesIO, Path], ) -> None: if backend is None: @@ -436,18 +441,23 @@ class ConversionResult(BaseModel): return ds_doc def render_element_images( - self, element_types: Tuple[PageElement] = (FigureElement,) + self, element_types: Tuple[Type[PageElement]] = (FigureElement,) ): for element in self.assembled.elements: if isinstance(element, element_types): page_ix = element.page_no - scale = self.pages[page_ix]._default_image_scale - crop_bbox = element.cluster.bbox.scaled(scale=scale).to_top_left_origin( - page_height=self.pages[page_ix].size.height * scale - ) + page = self.pages[page_ix] - cropped_im = self.pages[page_ix].image.crop(crop_bbox.as_tuple()) - yield element, cropped_im + assert page.size is not None + + scale = page._default_image_scale + crop_bbox = element.cluster.bbox.scaled(scale=scale).to_top_left_origin( + page_height=page.size.height * scale + ) + page_img = page.image + if page_img is not None: + cropped_im = page_img.crop(crop_bbox.as_tuple()) + yield element, cropped_im class _DocumentConversionInput(BaseModel): @@ -467,7 +477,7 @@ class _DocumentConversionInput(BaseModel): ) continue else: - backend = format_options.get(format).backend + backend = format_options[format].backend if isinstance(obj, Path): yield InputDocument( diff --git a/docling/document_converter.py b/docling/document_converter.py index c3bc2dac..017a2096 100644 --- a/docling/document_converter.py +++ b/docling/document_converter.py @@ -161,6 +161,8 @@ class DocumentConverter: def _convert( self, conv_input: _DocumentConversionInput, raises_on_error: bool ) -> Iterator[ConversionResult]: + assert self.format_to_options is not None + for input_batch in chunkify( conv_input.docs(self.format_to_options), settings.perf.doc_batch_size, # pass format_options @@ -181,6 +183,8 @@ class DocumentConverter: yield item def _get_pipeline(self, doc: InputDocument) -> Optional[BasePipeline]: + assert self.format_to_options is not None + fopt = self.format_to_options.get(doc.format) if fopt is None: @@ -189,6 +193,7 @@ class DocumentConverter: pipeline_class = fopt.pipeline_cls pipeline_options = fopt.pipeline_options + assert pipeline_options is not None # TODO this will ignore if different options have been defined for the same pipeline class. if ( pipeline_class not in self.initialized_pipelines @@ -202,7 +207,9 @@ class DocumentConverter: def process_document( self, in_doc: InputDocument, raises_on_error: bool - ) -> ConversionResult: + ) -> Optional[ConversionResult]: + assert self.allowed_formats is not None + if in_doc.format not in self.allowed_formats: return None else: @@ -217,7 +224,7 @@ class DocumentConverter: def _execute_pipeline( self, in_doc: InputDocument, raises_on_error: bool - ) -> Optional[ConversionResult]: + ) -> ConversionResult: if in_doc.valid: pipeline = self._get_pipeline(in_doc) if pipeline is None: # Can't find a default pipeline. Should this raise? diff --git a/docling/models/base_ocr_model.py b/docling/models/base_ocr_model.py index bd293dcd..c1ca8ef7 100644 --- a/docling/models/base_ocr_model.py +++ b/docling/models/base_ocr_model.py @@ -21,8 +21,9 @@ class BaseOcrModel: self.options = options # Computes the optimum amount and coordinates of rectangles to OCR on a given page - def get_ocr_rects(self, page: Page) -> Tuple[bool, List[BoundingBox]]: + def get_ocr_rects(self, page: Page) -> List[BoundingBox]: BITMAP_COVERAGE_TRESHOLD = 0.75 + assert page.size is not None def find_ocr_rects(size, bitmap_rects): image = Image.new( @@ -61,7 +62,10 @@ class BaseOcrModel: return (area_frac, bounding_boxes) # fraction covered # boxes - bitmap_rects = page._backend.get_bitmap_rects() + if page._backend is not None: + bitmap_rects = page._backend.get_bitmap_rects() + else: + bitmap_rects = [] coverage, ocr_rects = find_ocr_rects(page.size, bitmap_rects) # return full-page rectangle if sufficiently covered with bitmaps @@ -76,7 +80,7 @@ class BaseOcrModel: ) ] # return individual rectangles if the bitmap coverage is smaller - elif coverage < BITMAP_COVERAGE_TRESHOLD: + else: # coverage <= BITMAP_COVERAGE_TRESHOLD: return ocr_rects # Filters OCR cells by dropping any OCR cell that intersects with an existing programmatic cell. diff --git a/docling/models/ds_glm_model.py b/docling/models/ds_glm_model.py index 80ee87c8..635651ad 100644 --- a/docling/models/ds_glm_model.py +++ b/docling/models/ds_glm_model.py @@ -1,6 +1,6 @@ import copy import random -from typing import Tuple +from typing import List, Tuple, Union from deepsearch_glm.nlp_utils import init_nlp_model from deepsearch_glm.utils.doc_utils import ( @@ -42,7 +42,7 @@ class GlmModel: def _to_legacy_document(self, conv_res) -> DsDocument: title = "" - desc = DsDocumentDescription(logs=[]) + desc: DsDocumentDescription = DsDocumentDescription(logs=[]) page_hashes = [ PageReference( @@ -60,9 +60,9 @@ class GlmModel: page_hashes=page_hashes, ) - main_text = [] - tables = [] - figures = [] + main_text: List[Union[Ref, BaseText]] = [] + tables: List[DsSchemaTable] = [] + figures: List[Figure] = [] page_no_to_page = {p.page_no: p for p in conv_res.pages} @@ -146,11 +146,16 @@ class GlmModel: yield [rspan, cspan] spans = list(make_spans(cell)) + if cell.bbox is not None: + bbox = cell.bbox.to_bottom_left_origin( + page_no_to_page[element.page_no].size.height + ).as_tuple() + else: + bbox = None + table_data[i][j] = TableCell( text=cell.text, - bbox=cell.bbox.to_bottom_left_origin( - page_no_to_page[element.page_no].size.height - ).as_tuple(), + bbox=bbox, # col=j, # row=i, spans=spans, @@ -204,7 +209,7 @@ class GlmModel: for p in conv_res.pages ] - ds_doc = DsDocument( + ds_doc: DsDocument = DsDocument( name=title, description=desc, file_info=file_info, @@ -216,9 +221,7 @@ class GlmModel: return ds_doc - def __call__( - self, conv_res: ConversionResult - ) -> Tuple[DsLegacyDocument, DoclingDocument]: + def __call__(self, conv_res: ConversionResult) -> DoclingDocument: ds_doc = self._to_legacy_document(conv_res) ds_doc_dict = ds_doc.model_dump(by_alias=True) diff --git a/docling/models/easyocr_model.py b/docling/models/easyocr_model.py index 9e71fffc..0e02f11e 100644 --- a/docling/models/easyocr_model.py +++ b/docling/models/easyocr_model.py @@ -40,6 +40,8 @@ class EasyOcrModel(BaseOcrModel): return for page in page_batch: + assert page._backend is not None + ocr_rects = self.get_ocr_rects(page) all_ocr_cells = [] diff --git a/docling/models/layout_model.py b/docling/models/layout_model.py index 004be330..4572777d 100644 --- a/docling/models/layout_model.py +++ b/docling/models/layout_model.py @@ -47,7 +47,7 @@ class LayoutModel(BasePageModel): def __init__(self, artifacts_path: Path): self.layout_predictor = LayoutPredictor(artifacts_path) # TODO temporary - def postprocess(self, clusters: List[Cluster], cells: List[Cell], page_height): + def postprocess(self, clusters_in: List[Cluster], cells: List[Cell], page_height): MIN_INTERSECTION = 0.2 CLASS_THRESHOLDS = { DocItemLabel.CAPTION: 0.35, @@ -78,9 +78,9 @@ class LayoutModel(BasePageModel): start_time = time.time() # Apply Confidence Threshold to cluster predictions # confidence = self.conf_threshold - clusters_out = [] + clusters_mod = [] - for cluster in clusters: + for cluster in clusters_in: confidence = CLASS_THRESHOLDS[cluster.label] if cluster.confidence >= confidence: # annotation["created_by"] = "high_conf_pred" @@ -88,10 +88,10 @@ class LayoutModel(BasePageModel): # Remap class labels where needed. if cluster.label in CLASS_REMAPPINGS.keys(): cluster.label = CLASS_REMAPPINGS[cluster.label] - clusters_out.append(cluster) + clusters_mod.append(cluster) # map to dictionary clusters and cells, with bottom left origin - clusters = [ + clusters_orig = [ { "id": c.id, "bbox": list( @@ -101,7 +101,7 @@ class LayoutModel(BasePageModel): "cell_ids": [], "type": c.label, } - for c in clusters + for c in clusters_in ] clusters_out = [ @@ -115,9 +115,11 @@ class LayoutModel(BasePageModel): "cell_ids": [], "type": c.label, } - for c in clusters_out + for c in clusters_mod ] + del clusters_mod + raw_cells = [ { "id": c.id, @@ -151,7 +153,7 @@ class LayoutModel(BasePageModel): # Assign orphan cells with lower confidence predictions clusters_out, orphan_cell_indices = lu.assign_orphans_with_low_conf_pred( - clusters_out, clusters, raw_cells, orphan_cell_indices + clusters_out, clusters_orig, raw_cells, orphan_cell_indices ) # Refresh the cell_ids assignment, after creating new clusters using low conf predictions @@ -180,7 +182,7 @@ class LayoutModel(BasePageModel): ) = lu.cell_id_state_map(clusters_out, cell_count) clusters_out, orphan_cell_indices = lu.set_orphan_as_text( - clusters_out, clusters, raw_cells, orphan_cell_indices + clusters_out, clusters_orig, raw_cells, orphan_cell_indices ) _log.debug("---- 5. Merge Cells & and adapt the bounding boxes") @@ -239,34 +241,41 @@ class LayoutModel(BasePageModel): end_time = time.time() - start_time _log.debug(f"Finished post processing in seconds={end_time:.3f}") - cells_out = [ + cells_out_new = [ Cell( - id=c["id"], + id=c["id"], # type: ignore bbox=BoundingBox.from_tuple( - coord=c["bbox"], origin=CoordOrigin.BOTTOMLEFT + coord=c["bbox"], origin=CoordOrigin.BOTTOMLEFT # type: ignore ).to_top_left_origin(page_height), - text=c["text"], + text=c["text"], # type: ignore ) for c in cells_out ] + + del cells_out + clusters_out_new = [] for c in clusters_out: - cluster_cells = [ccell for ccell in cells_out if ccell.id in c["cell_ids"]] + cluster_cells = [ + ccell for ccell in cells_out_new if ccell.id in c["cell_ids"] # type: ignore + ] c_new = Cluster( - id=c["id"], + id=c["id"], # type: ignore bbox=BoundingBox.from_tuple( - coord=c["bbox"], origin=CoordOrigin.BOTTOMLEFT + coord=c["bbox"], origin=CoordOrigin.BOTTOMLEFT # type: ignore ).to_top_left_origin(page_height), - confidence=c["confidence"], + confidence=c["confidence"], # type: ignore label=DocItemLabel(c["type"]), cells=cluster_cells, ) clusters_out_new.append(c_new) - return clusters_out_new, cells_out + return clusters_out_new, cells_out_new def __call__(self, page_batch: Iterable[Page]) -> Iterable[Page]: for page in page_batch: + assert page.size is not None + clusters = [] for ix, pred_item in enumerate( self.layout_predictor.predict(page.get_image(scale=1.0)) diff --git a/docling/models/page_assemble_model.py b/docling/models/page_assemble_model.py index 780cc89c..6bd55bf0 100644 --- a/docling/models/page_assemble_model.py +++ b/docling/models/page_assemble_model.py @@ -53,6 +53,8 @@ class PageAssembleModel(BasePageModel): def __call__(self, page_batch: Iterable[Page]) -> Iterable[Page]: for page in page_batch: + assert page._backend is not None + assert page.predictions.layout is not None # assembles some JSON output page by page. elements: List[PageElement] = [] diff --git a/docling/models/page_preprocessing_model.py b/docling/models/page_preprocessing_model.py index fe079c45..7c71fd50 100644 --- a/docling/models/page_preprocessing_model.py +++ b/docling/models/page_preprocessing_model.py @@ -40,7 +40,9 @@ class PagePreprocessingModel(BasePageModel): # Extract and populate the page cells and store it in the page object def _parse_page_cells(self, page: Page) -> Page: - page.cells = page._backend.get_text_cells() + assert page._backend is not None + + page.cells = list(page._backend.get_text_cells()) # DEBUG code: def draw_text_boxes(image, cells): diff --git a/docling/models/table_structure_model.py b/docling/models/table_structure_model.py index 5445a0b9..857dfc1e 100644 --- a/docling/models/table_structure_model.py +++ b/docling/models/table_structure_model.py @@ -24,8 +24,6 @@ class TableStructureModel(BasePageModel): self.enabled = enabled if self.enabled: - artifacts_path: Path = artifacts_path - if self.mode == TableFormerMode.ACCURATE: artifacts_path = artifacts_path / "fat" @@ -40,6 +38,8 @@ class TableStructureModel(BasePageModel): self.scale = 2.0 # Scale up table input images to 144 dpi def draw_table_and_cells(self, page: Page, tbl_list: List[Table]): + assert page._backend is not None + image = ( page._backend.get_page_image() ) # make new image to avoid drawing on the saved ones @@ -50,17 +50,18 @@ class TableStructureModel(BasePageModel): draw.rectangle([(x0, y0), (x1, y1)], outline="red") for tc in table_element.table_cells: - x0, y0, x1, y1 = tc.bbox.as_tuple() - if tc.column_header: - width = 3 - else: - width = 1 - draw.rectangle([(x0, y0), (x1, y1)], outline="blue", width=width) - draw.text( - (x0 + 3, y0 + 3), - text=f"{tc.start_row_offset_idx}, {tc.start_col_offset_idx}", - fill="black", - ) + if tc.bbox is not None: + x0, y0, x1, y1 = tc.bbox.as_tuple() + if tc.column_header: + width = 3 + else: + width = 1 + draw.rectangle([(x0, y0), (x1, y1)], outline="blue", width=width) + draw.text( + (x0 + 3, y0 + 3), + text=f"{tc.start_row_offset_idx}, {tc.start_col_offset_idx}", + fill="black", + ) image.show() @@ -71,6 +72,9 @@ class TableStructureModel(BasePageModel): return for page in page_batch: + assert page._backend is not None + assert page.predictions.layout is not None + assert page.size is not None page.predictions.tablestructure = TableStructurePrediction() # dummy @@ -132,7 +136,7 @@ class TableStructureModel(BasePageModel): element["bbox"]["token"] = text_piece tc = TableCell.model_validate(element) - if self.do_cell_matching: + if self.do_cell_matching and tc.bbox is not None: tc.bbox = tc.bbox.scaled(1 / self.scale) table_cells.append(tc) diff --git a/docling/models/tesseract_ocr_cli_model.py b/docling/models/tesseract_ocr_cli_model.py index 6ff0c35c..2c416d97 100644 --- a/docling/models/tesseract_ocr_cli_model.py +++ b/docling/models/tesseract_ocr_cli_model.py @@ -2,7 +2,7 @@ import io import logging import tempfile from subprocess import DEVNULL, PIPE, Popen -from typing import Iterable, Tuple +from typing import Iterable, Optional, Tuple import pandas as pd from docling_core.types.doc import BoundingBox, CoordOrigin @@ -22,8 +22,8 @@ class TesseractOcrCliModel(BaseOcrModel): self.scale = 3 # multiplier for 72 dpi == 216 dpi. - self._name = None - self._version = None + self._name: Optional[str] = None + self._version: Optional[str] = None if self.enabled: try: @@ -40,7 +40,7 @@ class TesseractOcrCliModel(BaseOcrModel): def _get_name_and_version(self) -> Tuple[str, str]: if self._name != None and self._version != None: - return self._name, self._version + return self._name, self._version # type: ignore cmd = [self.options.tesseract_cmd, "--version"] @@ -109,6 +109,8 @@ class TesseractOcrCliModel(BaseOcrModel): return for page in page_batch: + assert page._backend is not None + ocr_rects = self.get_ocr_rects(page) all_ocr_cells = [] diff --git a/docling/models/tesseract_ocr_model.py b/docling/models/tesseract_ocr_model.py index c31981be..ea74b6ad 100644 --- a/docling/models/tesseract_ocr_model.py +++ b/docling/models/tesseract_ocr_model.py @@ -69,6 +69,9 @@ class TesseractOcrModel(BaseOcrModel): return for page in page_batch: + assert page._backend is not None + assert self.reader is not None + ocr_rects = self.get_ocr_rects(page) all_ocr_cells = [] diff --git a/docling/pipeline/base_pipeline.py b/docling/pipeline/base_pipeline.py index c903d569..5e26fe0c 100644 --- a/docling/pipeline/base_pipeline.py +++ b/docling/pipeline/base_pipeline.py @@ -178,7 +178,7 @@ class PaginatedPipeline(BasePipeline): # TODO this is a bad name. ) -> ConversionStatus: status = ConversionStatus.SUCCESS for page in conv_res.pages: - if not page._backend.is_valid(): + if page._backend is None or not page._backend.is_valid(): conv_res.errors.append( ErrorItem( component_type=DoclingComponentType.DOCUMENT_BACKEND, diff --git a/docling/pipeline/standard_pdf_pipeline.py b/docling/pipeline/standard_pdf_pipeline.py index 6d72884d..3f008762 100644 --- a/docling/pipeline/standard_pdf_pipeline.py +++ b/docling/pipeline/standard_pdf_pipeline.py @@ -123,8 +123,9 @@ class StandardPdfPipeline(PaginatedPipeline): return None def initialize_page(self, doc: InputDocument, page: Page) -> Page: - page._backend = doc._backend.load_page(page.page_no) - page.size = page._backend.get_size() + page._backend = doc._backend.load_page(page.page_no) # type: ignore + if page._backend is not None and page._backend.is_valid(): + page.size = page._backend.get_size() return page @@ -136,7 +137,7 @@ class StandardPdfPipeline(PaginatedPipeline): all_body = [] for p in conv_res.pages: - + assert p.assembled is not None for el in p.assembled.body: all_body.append(el) for el in p.assembled.headers: diff --git a/examples/rag_langchain.ipynb b/examples/rag_langchain.ipynb deleted file mode 100644 index 30e38329..00000000 --- a/examples/rag_langchain.ipynb +++ /dev/null @@ -1,369 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# RAG with Docling and 🦜🔗 LangChain" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "# requirements for this example:\n", - "%pip install -qq docling docling-core python-dotenv langchain-text-splitters langchain-huggingface langchain-milvus" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "\n", - "from dotenv import load_dotenv\n", - "\n", - "load_dotenv()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "warnings.filterwarnings(action=\"ignore\", category=UserWarning, module=\"pydantic|torch\")\n", - "warnings.filterwarnings(action=\"ignore\", category=FutureWarning, module=\"easyocr\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loader and splitter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below we set up:\n", - "- a `Loader` which will be used to create LangChain documents, and\n", - "- a splitter, which will be used to split these documents" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from enum import Enum\n", - "from typing import Iterator\n", - "\n", - "from langchain_core.document_loaders import BaseLoader\n", - "from langchain_core.documents import Document as LCDocument\n", - "from pydantic import BaseModel\n", - "\n", - "from docling.document_converter import DocumentConverter\n", - "\n", - "\n", - "class DocumentMetadata(BaseModel):\n", - " dl_doc_hash: str\n", - " # source: str\n", - "\n", - "\n", - "class DoclingPDFLoader(BaseLoader):\n", - " class ParseType(str, Enum):\n", - " MARKDOWN = \"markdown\"\n", - " # JSON = \"json\"\n", - "\n", - " def __init__(self, file_path: str | list[str], parse_type: ParseType) -> None:\n", - " self._file_paths = file_path if isinstance(file_path, list) else [file_path]\n", - " self._parse_type = parse_type\n", - " self._converter = DocumentConverter()\n", - "\n", - " def lazy_load(self) -> Iterator[LCDocument]:\n", - " for source in self._file_paths:\n", - " dl_doc = self._converter.convert_single(source).output\n", - " match self._parse_type:\n", - " case self.ParseType.MARKDOWN:\n", - " text = dl_doc.export_to_markdown()\n", - " # case self.ParseType.JSON:\n", - " # text = dl_doc.model_dump_json()\n", - " case _:\n", - " raise RuntimeError(\n", - " f\"Unexpected parse type encountered: {self._parse_type}\"\n", - " )\n", - " lc_doc = LCDocument(\n", - " page_content=text,\n", - " metadata=DocumentMetadata(\n", - " dl_doc_hash=dl_doc.file_info.document_hash,\n", - " ).model_dump(),\n", - " )\n", - " yield lc_doc" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "FILE_PATH = \"https://arxiv.org/pdf/2206.01062\" # DocLayNet paper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1b38d07d5fed4618a44ecf261e1e5c44", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Fetching 7 files: 0%| | 0/7 [00:00\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# RAG with Docling and 🦙 LlamaIndex" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "LlamaIndex extensions `DoclingReader` and `DoclingNodeParser` presented in this notebook seamlessly integrate Docling into LlamaIndex, enabling you to:\n", - "- use PDF documents in your LLM applications with ease and speed, and\n", - "- leverage Docling's rich format for advanced, document-native grounding." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- 👉 For best conversion speed, use GPU acceleration whenever available; e.g. if running on Colab, use GPU-enabled runtime.\n", - "- Notebook uses HuggingFace's Inference API; for increased LLM quota, token can be provided via env var `HF_TOKEN`.\n", - "- Requirements can be installed as shown below (`--no-warn-conflicts` meant for Colab's pre-populated Python env; feel free to remove for stricter usage):" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install -q --progress-bar off --no-warn-conflicts llama-index-core llama-index-readers-docling llama-index-node-parser-docling llama-index-embeddings-huggingface llama-index-llms-huggingface-api llama-index-readers-file python-dotenv" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from pathlib import Path\n", - "from tempfile import mkdtemp\n", - "from warnings import filterwarnings\n", - "\n", - "from dotenv import load_dotenv\n", - "\n", - "\n", - "def _get_env_from_colab_or_os(key):\n", - " try:\n", - " from google.colab import userdata\n", - "\n", - " try:\n", - " return userdata.get(key)\n", - " except userdata.SecretNotFoundError:\n", - " pass\n", - " except ImportError:\n", - " pass\n", - " return os.getenv(key)\n", - "\n", - "\n", - "load_dotenv()\n", - "\n", - "filterwarnings(action=\"ignore\", category=UserWarning, module=\"pydantic\")\n", - "filterwarnings(action=\"ignore\", category=FutureWarning, module=\"easyocr\")\n", - "# https://github.com/huggingface/transformers/issues/5486:\n", - "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now define the main parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n", - "from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n", - "\n", - "EMBED_MODEL = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n", - "MILVUS_URI = str(Path(mkdtemp()) / \"docling.db\")\n", - "GEN_MODEL = HuggingFaceInferenceAPI(\n", - " token=_get_env_from_colab_or_os(\"HF_TOKEN\"),\n", - " model_name=\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n", - ")\n", - "SOURCE = \"https://arxiv.org/pdf/2408.09869\" # Docling Technical Report\n", - "QUERY = \"Which are the main AI models in Docling?\"\n", - "\n", - "embed_dim = len(EMBED_MODEL.get_text_embedding(\"hi\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using Markdown export" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To create a simple RAG pipeline, we can:\n", - "- define a `DoclingReader`, which by default exports to Markdown, and\n", - "- use a standard node parser for these Markdown-based docs, e.g. a `MarkdownNodeParser`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Q: Which are the main AI models in Docling?\n", - "A: 1. A layout analysis model, an accurate object-detector for page elements. 2. TableFormer, a state-of-the-art table structure recognition model.\n", - "\n", - "Sources:\n" - ] - }, - { - "data": { - "text/plain": [ - "[('3.2 AI models\\n\\nAs part of Docling, we initially release two highly capable AI models to the open-source community, which have been developed and published recently by our team. The first model is a layout analysis model, an accurate object-detector for page elements [13]. The second model is TableFormer [12, 9], a state-of-the-art table structure recognition model. We provide the pre-trained weights (hosted on huggingface) and a separate package for the inference code as docling-ibm-models . Both models are also powering the open-access deepsearch-experience, our cloud-native service for knowledge exploration tasks.',\n", - " {'dl_doc_hash': '556ad9e23b6d2245e36b3208758cf0c8a709382bb4c859eacfe8e73b14e635aa',\n", - " 'Header_2': '3.2 AI models'}),\n", - " (\"5 Applications\\n\\nThanks to the high-quality, richly structured document conversion achieved by Docling, its output qualifies for numerous downstream applications. For example, Docling can provide a base for detailed enterprise document search, passage retrieval or classification use-cases, or support knowledge extraction pipelines, allowing specific treatment of different structures in the document, such as tables, figures, section structure or references. For popular generative AI application patterns, such as retrieval-augmented generation (RAG), we provide quackling , an open-source package which capitalizes on Docling's feature-rich document output to enable document-native optimized vector embedding and chunking. It plugs in seamlessly with LLM frameworks such as LlamaIndex [8]. Since Docling is fast, stable and cheap to run, it also makes for an excellent choice to build document-derived datasets. With its powerful table structure recognition, it provides significant benefit to automated knowledge-base construction [11, 10]. Docling is also integrated within the open IBM data prep kit [6], which implements scalable data transforms to build large-scale multi-modal training datasets.\",\n", - " {'dl_doc_hash': '556ad9e23b6d2245e36b3208758cf0c8a709382bb4c859eacfe8e73b14e635aa',\n", - " 'Header_2': '5 Applications'})]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from llama_index.core import StorageContext, VectorStoreIndex\n", - "from llama_index.core.node_parser import MarkdownNodeParser\n", - "from llama_index.readers.docling import DoclingReader\n", - "from llama_index.vector_stores.milvus import MilvusVectorStore\n", - "\n", - "reader = DoclingReader()\n", - "node_parser = MarkdownNodeParser()\n", - "\n", - "vector_store = MilvusVectorStore(\n", - " uri=str(Path(mkdtemp()) / \"docling.db\"), # or set as needed\n", - " dim=embed_dim,\n", - " overwrite=True,\n", - ")\n", - "index = VectorStoreIndex.from_documents(\n", - " documents=reader.load_data(SOURCE),\n", - " transformations=[node_parser],\n", - " storage_context=StorageContext.from_defaults(vector_store=vector_store),\n", - " embed_model=EMBED_MODEL,\n", - ")\n", - "result = index.as_query_engine(llm=GEN_MODEL).query(QUERY)\n", - "print(f\"Q: {QUERY}\\nA: {result.response.strip()}\\n\\nSources:\")\n", - "display([(n.text, n.metadata) for n in result.source_nodes])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using Docling format" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To leverage Docling's rich native format, we:\n", - "- create a `DoclingReader` with JSON export type, and\n", - "- employ a `DoclingNodeParser` in order to appropriately parse that Docling format.\n", - "\n", - "Notice how the sources now also contain document-level grounding (e.g. page number or bounding box information):" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Q: Which are the main AI models in Docling?\n", - "A: The main AI models in Docling are a layout analysis model and TableFormer. The layout analysis model is an accurate object-detector for page elements, and TableFormer is a state-of-the-art table structure recognition model.\n", - "\n", - "Sources:\n" - ] - }, - { - "data": { - "text/plain": [ - "[('As part of Docling, we initially release two highly capable AI models to the open-source community, which have been developed and published recently by our team. The first model is a layout analysis model, an accurate object-detector for page elements [13]. The second model is TableFormer [12, 9], a state-of-the-art table structure recognition model. We provide the pre-trained weights (hosted on huggingface) and a separate package for the inference code as docling-ibm-models . Both models are also powering the open-access deepsearch-experience, our cloud-native service for knowledge exploration tasks.',\n", - " {'dl_doc_hash': '556ad9e23b6d2245e36b3208758cf0c8a709382bb4c859eacfe8e73b14e635aa',\n", - " 'path': '#/main-text/37',\n", - " 'heading': '3.2 AI models',\n", - " 'page': 3,\n", - " 'bbox': [107.36903381347656,\n", - " 330.07513427734375,\n", - " 506.29705810546875,\n", - " 407.3725280761719]}),\n", - " ('With Docling , we open-source a very capable and efficient document conversion tool which builds on the powerful, specialized AI models and datasets for layout analysis and table structure recognition we developed and presented in the recent past [12, 13, 9]. Docling is designed as a simple, self-contained python library with permissive license, running entirely locally on commodity hardware. Its code architecture allows for easy extensibility and addition of new features and models.',\n", - " {'dl_doc_hash': '556ad9e23b6d2245e36b3208758cf0c8a709382bb4c859eacfe8e73b14e635aa',\n", - " 'path': '#/main-text/10',\n", - " 'heading': '1 Introduction',\n", - " 'page': 1,\n", - " 'bbox': [107.33261108398438,\n", - " 83.3067626953125,\n", - " 504.0033874511719,\n", - " 136.45367431640625]})]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from llama_index.node_parser.docling import DoclingNodeParser\n", - "\n", - "reader = DoclingReader(export_type=DoclingReader.ExportType.JSON)\n", - "node_parser = DoclingNodeParser()\n", - "\n", - "vector_store = MilvusVectorStore(\n", - " uri=str(Path(mkdtemp()) / \"docling.db\"), # or set as needed\n", - " dim=embed_dim,\n", - " overwrite=True,\n", - ")\n", - "index = VectorStoreIndex.from_documents(\n", - " documents=reader.load_data(SOURCE),\n", - " transformations=[node_parser],\n", - " storage_context=StorageContext.from_defaults(vector_store=vector_store),\n", - " embed_model=EMBED_MODEL,\n", - ")\n", - "result = index.as_query_engine(llm=GEN_MODEL).query(QUERY)\n", - "print(f\"Q: {QUERY}\\nA: {result.response.strip()}\\n\\nSources:\")\n", - "display([(n.text, n.metadata) for n in result.source_nodes])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## With Simple Directory Reader" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To demonstrate this usage pattern, we first set up a test document directory." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "from tempfile import mkdtemp\n", - "\n", - "import requests\n", - "\n", - "tmp_dir_path = Path(mkdtemp())\n", - "r = requests.get(SOURCE)\n", - "with open(tmp_dir_path / f\"{Path(SOURCE).name}.pdf\", \"wb\") as out_file:\n", - " out_file.write(r.content)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the `reader` and `node_parser` definitions from any of the above variants, usage with `SimpleDirectoryReader` then looks as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Loading files: 100%|██████████| 1/1 [00:11<00:00, 11.15s/file]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Q: Which are the main AI models in Docling?\n", - "A: The main AI models in Docling are a layout analysis model and TableFormer. The layout analysis model is an accurate object-detector for page elements, and TableFormer is a state-of-the-art table structure recognition model.\n", - "\n", - "Sources:\n" - ] - }, - { - "data": { - "text/plain": [ - "[('As part of Docling, we initially release two highly capable AI models to the open-source community, which have been developed and published recently by our team. The first model is a layout analysis model, an accurate object-detector for page elements [13]. The second model is TableFormer [12, 9], a state-of-the-art table structure recognition model. We provide the pre-trained weights (hosted on huggingface) and a separate package for the inference code as docling-ibm-models . Both models are also powering the open-access deepsearch-experience, our cloud-native service for knowledge exploration tasks.',\n", - " {'file_path': '/var/folders/76/4wwfs06x6835kcwj4186c0nc0000gn/T/tmp4vsev3_r/2408.09869.pdf',\n", - " 'file_name': '2408.09869.pdf',\n", - " 'file_type': 'application/pdf',\n", - " 'file_size': 5566574,\n", - " 'creation_date': '2024-10-09',\n", - " 'last_modified_date': '2024-10-09',\n", - " 'dl_doc_hash': '556ad9e23b6d2245e36b3208758cf0c8a709382bb4c859eacfe8e73b14e635aa',\n", - " 'path': '#/main-text/37',\n", - " 'heading': '3.2 AI models',\n", - " 'page': 3,\n", - " 'bbox': [107.36903381347656,\n", - " 330.07513427734375,\n", - " 506.29705810546875,\n", - " 407.3725280761719]}),\n", - " ('With Docling , we open-source a very capable and efficient document conversion tool which builds on the powerful, specialized AI models and datasets for layout analysis and table structure recognition we developed and presented in the recent past [12, 13, 9]. Docling is designed as a simple, self-contained python library with permissive license, running entirely locally on commodity hardware. Its code architecture allows for easy extensibility and addition of new features and models.',\n", - " {'file_path': '/var/folders/76/4wwfs06x6835kcwj4186c0nc0000gn/T/tmp4vsev3_r/2408.09869.pdf',\n", - " 'file_name': '2408.09869.pdf',\n", - " 'file_type': 'application/pdf',\n", - " 'file_size': 5566574,\n", - " 'creation_date': '2024-10-09',\n", - " 'last_modified_date': '2024-10-09',\n", - " 'dl_doc_hash': '556ad9e23b6d2245e36b3208758cf0c8a709382bb4c859eacfe8e73b14e635aa',\n", - " 'path': '#/main-text/10',\n", - " 'heading': '1 Introduction',\n", - " 'page': 1,\n", - " 'bbox': [107.33261108398438,\n", - " 83.3067626953125,\n", - " 504.0033874511719,\n", - " 136.45367431640625]})]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from llama_index.core import SimpleDirectoryReader\n", - "\n", - "dir_reader = SimpleDirectoryReader(\n", - " input_dir=tmp_dir_path,\n", - " file_extractor={\".pdf\": reader},\n", - ")\n", - "\n", - "vector_store = MilvusVectorStore(\n", - " uri=str(Path(mkdtemp()) / \"docling.db\"), # or set as needed\n", - " dim=embed_dim,\n", - " overwrite=True,\n", - ")\n", - "index = VectorStoreIndex.from_documents(\n", - " documents=dir_reader.load_data(SOURCE),\n", - " transformations=[node_parser],\n", - " storage_context=StorageContext.from_defaults(vector_store=vector_store),\n", - " embed_model=EMBED_MODEL,\n", - ")\n", - "result = index.as_query_engine(llm=GEN_MODEL).query(QUERY)\n", - "print(f\"Q: {QUERY}\\nA: {result.response.strip()}\\n\\nSources:\")\n", - "display([(n.text, n.metadata) for n in result.source_nodes])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/pyproject.toml b/pyproject.toml index 92225813..12e485d8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -114,6 +114,7 @@ py_version=311 pretty = true # strict = true no_implicit_optional = true +plugins = "pydantic.mypy" python_version = "3.10" [[tool.mypy.overrides]] @@ -121,6 +122,15 @@ module = [ "docling_parse.*", "pypdfium2.*", "networkx.*", + "scipy.*", + "filetype.*", + "tesserocr.*", + "docling_ibm_models.*", + "easyocr.*", + "deepsearch_glm.*", + "lxml.*", + "bs4.*", + "huggingface_hub.*" ] ignore_missing_imports = true diff --git a/tests/test_backend_docling_parse_v2.py b/tests/test_backend_docling_parse_v2.py index 8c4252cb..f27ecb43 100644 --- a/tests/test_backend_docling_parse_v2.py +++ b/tests/test_backend_docling_parse_v2.py @@ -26,7 +26,6 @@ def _get_backend(pdf_doc): return doc_backend -@pytest.mark.skip def test_text_cell_counts(): pdf_doc = Path("./tests/data/redp5695.pdf") From ba9eaf1bd719b2769679580006d988a6b622793a Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Tue, 15 Oct 2024 15:58:39 +0200 Subject: [PATCH 06/10] CLI and error handling fixes Signed-off-by: Christoph Auer --- docling/backend/html_backend.py | 2 +- docling/backend/mspowerpoint_backend.py | 4 +- docling/backend/msword_backend.py | 4 +- docling/cli/main.py | 30 ++++++++++----- docling/datamodel/document.py | 6 +-- docling/document_converter.py | 51 ++++++++++++++++--------- docling/pipeline/base_pipeline.py | 8 +--- 7 files changed, 64 insertions(+), 41 deletions(-) diff --git a/docling/backend/html_backend.py b/docling/backend/html_backend.py index 7878d64f..c7e68681 100644 --- a/docling/backend/html_backend.py +++ b/docling/backend/html_backend.py @@ -393,7 +393,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend): if contains_lists is None: return cell.text else: - _log.warn( + _log.debug( "should extract the content correctly for table-cells with lists ..." ) return cell.text diff --git a/docling/backend/mspowerpoint_backend.py b/docling/backend/mspowerpoint_backend.py index d50287f5..876a10e1 100644 --- a/docling/backend/mspowerpoint_backend.py +++ b/docling/backend/mspowerpoint_backend.py @@ -157,9 +157,9 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB new_list = None if is_a_list: - _log.info("LIST DETECTED!") + _log.debug("LIST DETECTED!") else: - _log.info("No List") + _log.debug("No List") # for e in p.iter(): for e in p.iterfind(".//a:r", namespaces={"a": self.namespaces["a"]}): diff --git a/docling/backend/msword_backend.py b/docling/backend/msword_backend.py index cc0e2613..54136fdd 100644 --- a/docling/backend/msword_backend.py +++ b/docling/backend/msword_backend.py @@ -138,7 +138,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend): try: self.handle_tables(element, docx_obj, doc) except Exception: - _log.error("could not parse a table, broken docx table") + _log.debug("could not parse a table, broken docx table") elif found_drawing or found_pict: self.handle_pictures(element, docx_obj, doc) @@ -146,7 +146,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend): elif tag_name in ["p"]: self.handle_text_elements(element, docx_obj, doc) else: - _log.warn(f"Ignoring element in DOCX with tag: {tag_name}") + _log.debug(f"Ignoring element in DOCX with tag: {tag_name}") return doc def str_to_int(self, s, default=0): diff --git a/docling/cli/main.py b/docling/cli/main.py index 6610cef2..f97e4938 100644 --- a/docling/cli/main.py +++ b/docling/cli/main.py @@ -5,7 +5,7 @@ import time import warnings from enum import Enum from pathlib import Path -from typing import Annotated, Iterable, List, Optional +from typing import Annotated, Dict, Iterable, List, Optional import typer from docling_core.utils.file import resolve_file_source @@ -26,7 +26,7 @@ from docling.datamodel.pipeline_options import ( TesseractCliOcrOptions, TesseractOcrOptions, ) -from docling.document_converter import DocumentConverter, PdfFormatOption +from docling.document_converter import DocumentConverter, FormatOption, PdfFormatOption warnings.filterwarnings(action="ignore", category=UserWarning, module="pydantic|torch") warnings.filterwarnings(action="ignore", category=FutureWarning, module="easyocr") @@ -152,6 +152,14 @@ def convert( ocr_engine: Annotated[ OcrEngine, typer.Option(..., help="The OCR engine to use.") ] = OcrEngine.EASYOCR, + abort_on_error: Annotated[ + bool, + typer.Option( + ..., + "--abort-on-error/--no-abort-on-error", + help="If enabled, the bitmap content will be processed using OCR.", + ), + ] = False, output: Annotated[ Path, typer.Option(..., help="Output directory where results are saved.") ] = Path("."), @@ -211,18 +219,22 @@ def convert( ) pipeline_options.table_structure_options.do_cell_matching = True # do_cell_matching + format_options: Dict[InputFormat, FormatOption] = { + InputFormat.PDF: PdfFormatOption( + pipeline_options=pipeline_options, + backend=DoclingParseDocumentBackend, # pdf_backend + ) + } doc_converter = DocumentConverter( - format_options={ - InputFormat.PDF: PdfFormatOption( - pipeline_options=pipeline_options, - backend=DoclingParseDocumentBackend, # pdf_backend - ) - } + allowed_formats=from_formats, + format_options=format_options, ) start_time = time.time() - conv_results = doc_converter.convert_all(input_doc_paths) + conv_results = doc_converter.convert_all( + input_doc_paths, raises_on_error=abort_on_error + ) output.mkdir(parents=True, exist_ok=True) export_documents( diff --git a/docling/datamodel/document.py b/docling/datamodel/document.py index bcc0254e..41d62114 100644 --- a/docling/datamodel/document.py +++ b/docling/datamodel/document.py @@ -160,7 +160,7 @@ class InputDocument(BaseModel): ) -> None: if backend is None: raise RuntimeError( - f"No backend configuration provided for file {self.file} with format {self.format}. " + f"No backend configuration provided for file {self.file.name} with format {self.format}. " f"Please check your format configuration on DocumentConverter." ) @@ -472,8 +472,8 @@ class _DocumentConversionInput(BaseModel): obj = resolve_file_source(item) if isinstance(item, str) else item format = self._guess_format(obj) if format not in format_options.keys(): - _log.debug( - f"Skipping input document {obj.name} because its format is not in the whitelist." + _log.info( + f"Skipping input document {obj.name} because it isn't matching any of the allowed formats." ) continue else: diff --git a/docling/document_converter.py b/docling/document_converter.py index 017a2096..a44dc9ce 100644 --- a/docling/document_converter.py +++ b/docling/document_converter.py @@ -111,6 +111,14 @@ class DocumentConverter: _log.debug(f"Requested format {f} will use default options.") self.format_to_options[f] = _format_to_default_options[f] + remove_keys = [] + for f in self.format_to_options.keys(): + if f not in self.allowed_formats: + remove_keys.append(f) + + for f in remove_keys: + self.format_to_options.pop(f) + self.initialized_pipelines: Dict[Type[BasePipeline], BasePipeline] = {} @validate_call(config=ConfigDict(strict=True)) @@ -176,7 +184,7 @@ class DocumentConverter: # Note: PDF backends are not thread-safe, thread pool usage was disabled. for item in map( - partial(self.process_document, raises_on_error=raises_on_error), + partial(self._process_document, raises_on_error=raises_on_error), input_batch, ): if item is not None: @@ -205,22 +213,22 @@ class DocumentConverter: ) return self.initialized_pipelines[pipeline_class] - def process_document( + def _process_document( self, in_doc: InputDocument, raises_on_error: bool ) -> Optional[ConversionResult]: assert self.allowed_formats is not None + assert in_doc.format in self.allowed_formats - if in_doc.format not in self.allowed_formats: - return None - else: - start_doc_time = time.time() + start_doc_time = time.time() - conv_res = self._execute_pipeline(in_doc, raises_on_error=raises_on_error) + conv_res = self._execute_pipeline(in_doc, raises_on_error=raises_on_error) - end_doc_time = time.time() - start_doc_time - _log.info(f"Finished converting document in {end_doc_time:.2f} seconds.") + end_doc_time = time.time() - start_doc_time + _log.info( + f"Finished converting document {in_doc.file.name} in {end_doc_time:.2f} seconds." + ) - return conv_res + return conv_res def _execute_pipeline( self, in_doc: InputDocument, raises_on_error: bool @@ -228,16 +236,25 @@ class DocumentConverter: if in_doc.valid: pipeline = self._get_pipeline(in_doc) if pipeline is None: # Can't find a default pipeline. Should this raise? - conv_res = ConversionResult(input=in_doc) - conv_res.status = ConversionStatus.FAILURE - return conv_res + if raises_on_error: + raise RuntimeError( + f"No pipeline could be initialized for {in_doc.file}." + ) + else: + conv_res = ConversionResult(input=in_doc) + conv_res.status = ConversionStatus.FAILURE + return conv_res conv_res = pipeline.execute(in_doc, raises_on_error=raises_on_error) else: - # invalid doc or not of desired format - conv_res = ConversionResult(input=in_doc) - conv_res.status = ConversionStatus.FAILURE - # TODO add error log why it failed. + if raises_on_error: + raise RuntimeError(f"Input document {in_doc.file} is not valid.") + + else: + # invalid doc or not of desired format + conv_res = ConversionResult(input=in_doc) + conv_res.status = ConversionStatus.FAILURE + # TODO add error log why it failed. return conv_res diff --git a/docling/pipeline/base_pipeline.py b/docling/pipeline/base_pipeline.py index 5e26fe0c..8dd074cc 100644 --- a/docling/pipeline/base_pipeline.py +++ b/docling/pipeline/base_pipeline.py @@ -34,12 +34,6 @@ class BasePipeline(ABC): conv_res = ConversionResult(input=in_doc) _log.info(f"Processing document {in_doc.file.name}") - - if not in_doc.valid: - conv_res.status = ConversionStatus.FAILURE - return conv_res - - # TODO: propagate option for raises_on_error? try: # These steps are building and assembling the structure of the # output DoclingDocument @@ -155,7 +149,7 @@ class PaginatedPipeline(BasePipeline): # TODO this is a bad name. pass end_pb_time = time.time() - start_pb_time - _log.info(f"Finished converting page batch time={end_pb_time:.3f}") + _log.debug(f"Finished converting page batch time={end_pb_time:.3f}") except Exception as e: conv_res.status = ConversionStatus.FAILURE From c1794a79e21cb3bd893f489e1d73889adcb2a7e1 Mon Sep 17 00:00:00 2001 From: Panos Vagenas <35837085+vagenas@users.noreply.github.com> Date: Tue, 15 Oct 2024 16:30:35 +0200 Subject: [PATCH 07/10] add v2 docs placeholder [skip ci] (#145) * add v2 docs placeholder [skip ci] Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> * Remove conflicts Signed-off-by: Christoph Auer --------- Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> Signed-off-by: Christoph Auer Co-authored-by: Christoph Auer --- docs/examples/rag_llamaindex.ipynb | 2 +- docs/overrides/main.html | 4 +--- docs/v2.md | 7 +++++++ mkdocs.yml | 2 +- 4 files changed, 10 insertions(+), 5 deletions(-) create mode 100644 docs/v2.md diff --git a/docs/examples/rag_llamaindex.ipynb b/docs/examples/rag_llamaindex.ipynb index 48ade368..bf52a0d8 100644 --- a/docs/examples/rag_llamaindex.ipynb +++ b/docs/examples/rag_llamaindex.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/docs/overrides/main.html b/docs/overrides/main.html index a60852d8..9071c5ff 100644 --- a/docs/overrides/main.html +++ b/docs/overrides/main.html @@ -1,7 +1,5 @@ {% extends "base.html" %} -{# {% block announce %} -

🎉 Docling is now officially supported in LlamaIndex! Check it out!

+

🎉 Docling is going v2, check out what's new and how to get started!

{% endblock %} -#} diff --git a/docs/v2.md b/docs/v2.md new file mode 100644 index 00000000..55135b1a --- /dev/null +++ b/docs/v2.md @@ -0,0 +1,7 @@ +## What's new + +Stay tuned! + +## Migration from v1 + +Stay tuned! diff --git a/mkdocs.yml b/mkdocs.yml index 2deb6463..f037962b 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -54,7 +54,7 @@ nav: - Get started: - Home: index.md - Installation: installation.md - # - Docling v2: v2.md + - Docling v2: v2.md # - Concepts: # - Docling Document: concepts/document.md # - Chunking: concepts/chunking.md From 40bb84d2decb8c4269d4b31b5733a4e8f6ed4035 Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Tue, 15 Oct 2024 16:34:45 +0200 Subject: [PATCH 08/10] Change output folder for examples. Signed-off-by: Christoph Auer --- docs/examples/batch_convert.py | 2 +- docs/examples/custom_convert.py | 2 +- docs/examples/export_figures.py | 2 +- docs/examples/export_multimodal.py | 2 +- docs/examples/export_tables.py | 2 +- docs/examples/run_with_formats.py | 2 +- examples/custom_convert.py | 1 - 7 files changed, 6 insertions(+), 7 deletions(-) delete mode 120000 examples/custom_convert.py diff --git a/docs/examples/batch_convert.py b/docs/examples/batch_convert.py index 73498915..da1c701f 100644 --- a/docs/examples/batch_convert.py +++ b/docs/examples/batch_convert.py @@ -122,7 +122,7 @@ def main(): raises_on_error=False, # to let conversion run through all and examine results at the end ) success_count, partial_success_count, failure_count = export_documents( - conv_results, output_dir=Path("../../examples/scratch") + conv_results, output_dir=Path("scratch") ) end_time = time.time() - start_time diff --git a/docs/examples/custom_convert.py b/docs/examples/custom_convert.py index 67d58a6b..8c753396 100644 --- a/docs/examples/custom_convert.py +++ b/docs/examples/custom_convert.py @@ -113,7 +113,7 @@ def main(): _log.info(f"Document converted in {end_time:.2f} seconds.") ## Export results - output_dir = Path("../../examples/scratch") + output_dir = Path("scratch") output_dir.mkdir(parents=True, exist_ok=True) doc_filename = conv_result.input.file.stem diff --git a/docs/examples/export_figures.py b/docs/examples/export_figures.py index d6fed16d..1110b0f1 100644 --- a/docs/examples/export_figures.py +++ b/docs/examples/export_figures.py @@ -15,7 +15,7 @@ def main(): logging.basicConfig(level=logging.INFO) input_doc_path = Path("./tests/data/2206.01062.pdf") - output_dir = Path("../../examples/scratch") + output_dir = Path("scratch") # Important: For operating with page images, we must keep them, otherwise the DocumentConverter # will destroy them for cleaning up memory. diff --git a/docs/examples/export_multimodal.py b/docs/examples/export_multimodal.py index 3646aea3..72845f41 100644 --- a/docs/examples/export_multimodal.py +++ b/docs/examples/export_multimodal.py @@ -20,7 +20,7 @@ def main(): logging.basicConfig(level=logging.INFO) input_doc_path = Path("./tests/data/2206.01062.pdf") - output_dir = Path("../../examples/scratch") + output_dir = Path("scratch") # Important: For operating with page images, we must keep them, otherwise the DocumentConverter # will destroy them for cleaning up memory. diff --git a/docs/examples/export_tables.py b/docs/examples/export_tables.py index ff962e80..68b9ce47 100644 --- a/docs/examples/export_tables.py +++ b/docs/examples/export_tables.py @@ -13,7 +13,7 @@ def main(): logging.basicConfig(level=logging.INFO) input_doc_path = Path("./tests/data/2206.01062.pdf") - output_dir = Path("../../examples/scratch") + output_dir = Path("scratch") doc_converter = DocumentConverter() diff --git a/docs/examples/run_with_formats.py b/docs/examples/run_with_formats.py index 4ae7055c..1767980c 100644 --- a/docs/examples/run_with_formats.py +++ b/docs/examples/run_with_formats.py @@ -53,7 +53,7 @@ doc_converter = ( conv_results = doc_converter.convert_all(input_paths) for res in conv_results: - out_path = Path("../../examples/scratch") + out_path = Path("scratch") print( f"Document {res.input.file.name} converted." f"\nSaved markdown output to: {str(out_path)}" diff --git a/examples/custom_convert.py b/examples/custom_convert.py deleted file mode 120000 index dc4e853e..00000000 --- a/examples/custom_convert.py +++ /dev/null @@ -1 +0,0 @@ -../docs/examples/custom_convert.py \ No newline at end of file From 9d15f4d5bff71864d8411d8382e55337a6485860 Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Tue, 15 Oct 2024 16:36:21 +0200 Subject: [PATCH 09/10] Adjust CI examples path Signed-off-by: Christoph Auer --- .github/workflows/checks.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml index 8c88acc5..6a124361 100644 --- a/.github/workflows/checks.yml +++ b/.github/workflows/checks.yml @@ -26,7 +26,7 @@ jobs: poetry run pytest -v tests - name: Run examples run: | - for file in examples/*.py; do + for file in docs/examples/*.py; do # Skip batch_convert.py if [[ "$(basename "$file")" == "batch_convert.py" ]]; then echo "Skipping $file" From 74e0452b6add931667f6034ba98a23e2804ac000 Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Tue, 15 Oct 2024 17:08:48 +0200 Subject: [PATCH 10/10] Add migration instructions to doc (wip) Signed-off-by: Christoph Auer --- docling/cli/main.py | 1 - docling/datamodel/document.py | 3 - docling/datamodel/pipeline_options.py | 5 +- docling/models/base_ocr_model.py | 2 +- docling/models/ds_glm_model.py | 8 +- docling/models/tesseract_ocr_model.py | 1 - docs/examples/batch_convert.py | 2 +- docs/examples/custom_convert.py | 12 +-- docs/examples/export_figures.py | 3 +- docs/examples/export_multimodal.py | 2 +- docs/examples/export_tables.py | 2 +- docs/v2.md | 104 +++++++++++++++++++++++++- tests/test_legacy_format_transform.py | 1 - 13 files changed, 116 insertions(+), 30 deletions(-) diff --git a/docling/cli/main.py b/docling/cli/main.py index f97e4938..1800ea18 100644 --- a/docling/cli/main.py +++ b/docling/cli/main.py @@ -11,7 +11,6 @@ import typer from docling_core.utils.file import resolve_file_source from docling.backend.docling_parse_backend import DoclingParseDocumentBackend -from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend from docling.datamodel.base_models import ( ConversionStatus, FormatToExtensions, diff --git a/docling/datamodel/document.py b/docling/datamodel/document.py index 41d62114..1b39ff7d 100644 --- a/docling/datamodel/document.py +++ b/docling/datamodel/document.py @@ -23,7 +23,6 @@ from docling_core.types.doc import ( TextItem, ) from docling_core.types.doc.document import ListItem -from docling_core.types.legacy_doc.base import BoundingBox as DsBoundingBox from docling_core.types.legacy_doc.base import Figure, GlmTableCell, TableCell from docling_core.utils.file import resolve_file_source from pydantic import BaseModel @@ -43,8 +42,6 @@ from docling.datamodel.base_models import ( MimeTypeToFormat, Page, PageElement, - Table, - TextElement, ) from docling.datamodel.settings import DocumentLimits from docling.utils.utils import create_file_hash, create_hash diff --git a/docling/datamodel/pipeline_options.py b/docling/datamodel/pipeline_options.py index 473ef980..ccb52c4a 100644 --- a/docling/datamodel/pipeline_options.py +++ b/docling/datamodel/pipeline_options.py @@ -1,9 +1,8 @@ -import warnings from enum import Enum, auto from pathlib import Path -from typing import Annotated, List, Literal, Optional, Union +from typing import List, Literal, Optional, Union -from pydantic import BaseModel, ConfigDict, Field, model_validator +from pydantic import BaseModel, ConfigDict, Field class TableFormerMode(str, Enum): diff --git a/docling/models/base_ocr_model.py b/docling/models/base_ocr_model.py index c1ca8ef7..59ae2295 100644 --- a/docling/models/base_ocr_model.py +++ b/docling/models/base_ocr_model.py @@ -1,7 +1,7 @@ import copy import logging from abc import abstractmethod -from typing import Iterable, List, Tuple +from typing import Iterable, List import numpy as np from docling_core.types.doc import BoundingBox, CoordOrigin diff --git a/docling/models/ds_glm_model.py b/docling/models/ds_glm_model.py index 635651ad..1b0a84e7 100644 --- a/docling/models/ds_glm_model.py +++ b/docling/models/ds_glm_model.py @@ -1,16 +1,12 @@ import copy import random -from typing import List, Tuple, Union +from typing import List, Union from deepsearch_glm.nlp_utils import init_nlp_model -from deepsearch_glm.utils.doc_utils import ( - to_docling_document, - to_legacy_document_format, -) +from deepsearch_glm.utils.doc_utils import to_docling_document from deepsearch_glm.utils.load_pretrained_models import load_pretrained_nlp_models from docling_core.types import BaseText from docling_core.types import Document as DsDocument -from docling_core.types import Document as DsLegacyDocument from docling_core.types import DocumentDescription as DsDocumentDescription from docling_core.types import FileInfoObject as DsFileInfoObject from docling_core.types import PageDimensions, PageReference, Prov, Ref diff --git a/docling/models/tesseract_ocr_model.py b/docling/models/tesseract_ocr_model.py index ea74b6ad..a97eb9a8 100644 --- a/docling/models/tesseract_ocr_model.py +++ b/docling/models/tesseract_ocr_model.py @@ -1,7 +1,6 @@ import logging from typing import Iterable -import numpy from docling_core.types.doc import BoundingBox, CoordOrigin from docling.datamodel.base_models import OcrCell, Page diff --git a/docs/examples/batch_convert.py b/docs/examples/batch_convert.py index da1c701f..c8e244fa 100644 --- a/docs/examples/batch_convert.py +++ b/docs/examples/batch_convert.py @@ -1,9 +1,9 @@ import json import logging -import time from pathlib import Path from typing import Iterable +import time import yaml from docling.datamodel.base_models import ConversionStatus diff --git a/docs/examples/custom_convert.py b/docs/examples/custom_convert.py index 8c753396..941cc951 100644 --- a/docs/examples/custom_convert.py +++ b/docs/examples/custom_convert.py @@ -1,18 +1,14 @@ import json import logging -import time from pathlib import Path -from typing import Iterable -from docling.datamodel.base_models import ConversionStatus, InputFormat -from docling.datamodel.document import ConversionResult +import time + +from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import ( PdfPipelineOptions, - TesseractCliOcrOptions, - TesseractOcrOptions, ) -from docling.document_converter import DocumentConverter, FormatOption, PdfFormatOption -from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline +from docling.document_converter import DocumentConverter, PdfFormatOption _log = logging.getLogger(__name__) diff --git a/docs/examples/export_figures.py b/docs/examples/export_figures.py index 1110b0f1..90f465b4 100644 --- a/docs/examples/export_figures.py +++ b/docs/examples/export_figures.py @@ -1,7 +1,8 @@ import logging -import time from pathlib import Path +import time + from docling.datamodel.base_models import FigureElement, InputFormat, Table from docling.datamodel.pipeline_options import PdfPipelineOptions from docling.document_converter import DocumentConverter, PdfFormatOption diff --git a/docs/examples/export_multimodal.py b/docs/examples/export_multimodal.py index 72845f41..5ead9a0a 100644 --- a/docs/examples/export_multimodal.py +++ b/docs/examples/export_multimodal.py @@ -1,9 +1,9 @@ import datetime import logging -import time from pathlib import Path import pandas as pd +import time from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import PdfPipelineOptions diff --git a/docs/examples/export_tables.py b/docs/examples/export_tables.py index 68b9ce47..55d3a40f 100644 --- a/docs/examples/export_tables.py +++ b/docs/examples/export_tables.py @@ -1,8 +1,8 @@ import logging -import time from pathlib import Path import pandas as pd +import time from docling.document_converter import DocumentConverter diff --git a/docs/v2.md b/docs/v2.md index 55135b1a..7e5e9ad9 100644 --- a/docs/v2.md +++ b/docs/v2.md @@ -1,7 +1,107 @@ ## What's new -Stay tuned! +Docling v2 introduces several new features: +- Understands and converts PDF, MS Word, MS Powerpoint, HTML and several image formats +- Produces a new, universal document representation which can encapsulate document hierarchy +- Comes with a fresh new API and CLI ## Migration from v1 -Stay tuned! +### Setting up a `DocumentConverter` + +To accomodate many input formats, we changed the way you need to set up your `DocumentConverter` object. +You can now define a list of allowed formats on the `DocumentConverter` initialization, and specify custom options +per-format if desired. By default, all supported formats are allowed. If you don't provide `format_options`, defaults +will be used for all `allowed_formats`. + +Format options can include the pipeline class to use, the options to provide to the pipeline, and the document backend. +They are provided as format-specific types, such as `PdfFormatOption` or `WordFormatOption`, as seen below. + +```python +from docling.document_converter import DocumentConverter +from docling.datamodel.base_models import InputFormat +from docling.document_converter import ( + DocumentConverter, + PdfFormatOption, + WordFormatOption, +) +from docling.pipeline.simple_pipeline import SimplePipeline +from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline +from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend + +## Default initialization still works as before: +# doc_converter = DocumentConverter() + +## Custom options are now defined per format. +doc_converter = ( + DocumentConverter( # all of the below is optional, has internal defaults. + allowed_formats=[ + InputFormat.PDF, + InputFormat.IMAGE, + InputFormat.DOCX, + InputFormat.HTML, + InputFormat.PPTX, + ], # whitelist formats, non-matching files are ignored. + format_options={ + InputFormat.PDF: PdfFormatOption( + pipeline_cls=StandardPdfPipeline, backend=PyPdfiumDocumentBackend + ), + InputFormat.DOCX: WordFormatOption( + pipeline_cls=SimplePipeline # , backend=MsWordDocumentBackend + ), + }, + ) +) +``` + +Note: If you work only with defaults, all remains the same as in Docling v1. + +### Converting documents + +We have simplified the way you can feed input to the `DocumentConverter` and renamed the conversion methods for +better semantics. You can now call the conversion directly with a single file, or a list of input files, +or `DocumentStream` objects, without constructing a `DocumentConversionInput` object first. + +* `DocumentConverter.convert` now converts a single file input (previously `DocumentConverter.convert_single`). +* `DocumentConverter.convert_all` now converts many files at once (previously `DocumentConverter.convert`). + + +```python +... +## Convert a single file (from URL or local path) +conv_result = doc_converter.convert("https://arxiv.org/pdf/2408.09869") # previously `convert_single` + +## Convert several files at once: + +input_files = [ + "tests/data/wiki_duck.html", + "tests/data/word_sample.docx", + "tests/data/lorem_ipsum.docx", + "tests/data/powerpoint_sample.pptx", + "tests/data/2305.03393v1-pg9-img.png", + "tests/data/2206.01062.pdf", +] + +conv_results_iter = doc_converter.convert_all(input_files) # previously `convert_batch` + +``` +Through the `raises_on_error` argument, you can also control if the conversion should raise exceptions when first +encountering a problem, or resiliently convert all files first and reflect errors in each file's conversion status. +By default, any error is immediately raised and the conversion aborts (previously, exceptions were swallowed). + +```python +... +conv_results_iter = doc_converter.convert_all(input_files, raises_on_error=False) # previously `convert_batch` + +``` + +### Exporting documents into JSON, Markdown, Doctags + +We have simplified how you can access and export the converted document data, too. + +TBD. + + +### CLI + +TBD. diff --git a/tests/test_legacy_format_transform.py b/tests/test_legacy_format_transform.py index 517b0b8b..4f764542 100644 --- a/tests/test_legacy_format_transform.py +++ b/tests/test_legacy_format_transform.py @@ -3,7 +3,6 @@ from pathlib import Path import pytest -from docling.backend.docling_parse_backend import DoclingParseDocumentBackend from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import PdfPipelineOptions from docling.document_converter import DocumentConverter, PdfFormatOption