From c84b973959a254db22ac9a7dc8810628e4808a2d Mon Sep 17 00:00:00 2001 From: Panos Vagenas <35837085+vagenas@users.noreply.github.com> Date: Tue, 25 Feb 2025 13:07:38 +0100 Subject: [PATCH 1/7] docs: extend chunking docs, add FAQ on token limit (#1053) Signed-off-by: Panos Vagenas --- docs/concepts/chunking.md | 13 +++++++++++++ docs/faq.md | 38 +++++++++++++++++++++++++++++++++++++- 2 files changed, 50 insertions(+), 1 deletion(-) diff --git a/docs/concepts/chunking.md b/docs/concepts/chunking.md index 94ae387e..c552f3aa 100644 --- a/docs/concepts/chunking.md +++ b/docs/concepts/chunking.md @@ -1,5 +1,18 @@ ## Introduction +!!! note "Chunking approaches" + + Starting from a `DoclingDocument`, there are in principle two possible chunking + approaches: + + 1. exporting the `DoclingDocument` to Markdown (or similar format) and then + performing user-defined chunking as a post-processing step, or + 2. using native Docling chunkers, i.e. operating directly on the `DoclingDocument` + + This page is about the latter, i.e. using native Docling chunkers. + For an example of using approach (1) check out e.g. + [this recipe](../examples/rag_langchain.ipynb) looking at the Markdown export mode. + A *chunker* is a Docling abstraction that, given a [`DoclingDocument`](./docling_document.md), returns a stream of chunks, each of which captures some part of the document as a string accompanied by respective metadata. diff --git a/docs/faq.md b/docs/faq.md index 9daccae6..5f54d863 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -132,9 +132,45 @@ This is a collection of FAQ collected from the user questions on Token indices sequence length is longer than the specified maximum sequence length for this model (530 > 512). Running this sequence through the model will result in indexing errors + + This is a warning that is emitted by transformers, saying that actually *running this sequence through the model* will result in indexing errors, i.e. the problematic case is only if one indeed passes the particular sequence through the (embedding) model. + + In our case though, this occurs as a "false alarm", since what happens is the following: + + - the chunker invokes the tokenizer on a potentially long sequence (e.g. 530 tokens as mentioned in the warning) in order to count its tokens, i.e. to assess if it is short enough. At this point transformers already emits the warning above! + - whenever the sequence at hand is oversized, the chunker proceeds to split it (but the transformers warning has already been shown nonetheless) + + What is important is the actual token length of the produced chunks. + The snippet below can be used for getting the actual maximum chunk size (for users wanting to confirm that this does not exceed the model limit): + + ```python + max_len = 0 + for i, chunk in enumerate(chunks): + ser_txt = chunker.serialize(chunk=chunk) + ser_tokens = len(tokenizer.tokenize(ser_txt, max_len_length=None)) + if ser_tokens > max_len: + max_len = ser_tokens + print(f"{i}\t{ser_tokens}\t{repr(ser_txt[:100])}...") + print(f"{max_len=}") + ``` + + Source: Issue [docling-core#119](https://github.com/DS4SD/docling-core/issues/119) From e1972257399151503d60b4806976c8b9b6911aa8 Mon Sep 17 00:00:00 2001 From: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> Date: Wed, 26 Feb 2025 08:33:50 +0100 Subject: [PATCH 2/7] fix: vlm using artifacts path (#1057) * fix usage of artifacts path Signed-off-by: Michele Dolfi * add granite vision to the download utils Signed-off-by: Michele Dolfi --------- Signed-off-by: Michele Dolfi --- docling/cli/models.py | 13 ++++++++++++- docling/models/picture_description_vlm_model.py | 4 ++-- docling/utils/model_downloader.py | 17 +++++++++++++++-- 3 files changed, 29 insertions(+), 5 deletions(-) diff --git a/docling/cli/models.py b/docling/cli/models.py index 3b62ad6b..50038adf 100644 --- a/docling/cli/models.py +++ b/docling/cli/models.py @@ -32,9 +32,19 @@ class _AvailableModels(str, Enum): CODE_FORMULA = "code_formula" PICTURE_CLASSIFIER = "picture_classifier" SMOLVLM = "smolvlm" + GRANITE_VISION = "granite_vision" EASYOCR = "easyocr" +_default_models = [ + _AvailableModels.LAYOUT, + _AvailableModels.TABLEFORMER, + _AvailableModels.CODE_FORMULA, + _AvailableModels.PICTURE_CLASSIFIER, + _AvailableModels.EASYOCR, +] + + @app.command("download") def download( output_dir: Annotated[ @@ -73,7 +83,7 @@ def download( datefmt="[%X]", handlers=[RichHandler(show_level=False, show_time=False, markup=True)], ) - to_download = models or [m for m in _AvailableModels] + to_download = models or _default_models output_dir = download_models( output_dir=output_dir, force=force, @@ -83,6 +93,7 @@ def download( with_code_formula=_AvailableModels.CODE_FORMULA in to_download, with_picture_classifier=_AvailableModels.PICTURE_CLASSIFIER in to_download, with_smolvlm=_AvailableModels.SMOLVLM in to_download, + with_granite_vision=_AvailableModels.GRANITE_VISION in to_download, with_easyocr=_AvailableModels.EASYOCR in to_download, ) diff --git a/docling/models/picture_description_vlm_model.py b/docling/models/picture_description_vlm_model.py index 9fa4826d..69d185b0 100644 --- a/docling/models/picture_description_vlm_model.py +++ b/docling/models/picture_description_vlm_model.py @@ -41,9 +41,9 @@ class PictureDescriptionVlmModel(PictureDescriptionBaseModel): ) # Initialize processor and model - self.processor = AutoProcessor.from_pretrained(self.options.repo_id) + self.processor = AutoProcessor.from_pretrained(artifacts_path) self.model = AutoModelForVision2Seq.from_pretrained( - self.options.repo_id, + artifacts_path, torch_dtype=torch.bfloat16, _attn_implementation=( "flash_attention_2" if self.device.startswith("cuda") else "eager" diff --git a/docling/utils/model_downloader.py b/docling/utils/model_downloader.py index 7d22b77b..694fe042 100644 --- a/docling/utils/model_downloader.py +++ b/docling/utils/model_downloader.py @@ -2,7 +2,10 @@ import logging from pathlib import Path from typing import Optional -from docling.datamodel.pipeline_options import smolvlm_picture_description +from docling.datamodel.pipeline_options import ( + granite_picture_description, + smolvlm_picture_description, +) from docling.datamodel.settings import settings from docling.models.code_formula_model import CodeFormulaModel from docling.models.document_picture_classifier import DocumentPictureClassifier @@ -23,7 +26,8 @@ def download_models( with_tableformer: bool = True, with_code_formula: bool = True, with_picture_classifier: bool = True, - with_smolvlm: bool = True, + with_smolvlm: bool = False, + with_granite_vision: bool = False, with_easyocr: bool = True, ): if output_dir is None: @@ -73,6 +77,15 @@ def download_models( progress=progress, ) + if with_granite_vision: + _log.info(f"Downloading Granite Vision model...") + PictureDescriptionVlmModel.download_models( + repo_id=granite_picture_description.repo_id, + local_dir=output_dir / granite_picture_description.repo_cache_folder, + force=force, + progress=progress, + ) + if with_easyocr: _log.info(f"Downloading easyocr models...") EasyOcrModel.download_models( From ab683e4fb6df4973d2efda04f00c269a2dc95f5b Mon Sep 17 00:00:00 2001 From: Panos Vagenas <35837085+vagenas@users.noreply.github.com> Date: Wed, 26 Feb 2025 13:27:29 +0100 Subject: [PATCH 3/7] feat(cli): add option for downloading all models, refine help messages (#1061) * chore(cli): update download help messages Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> * add `--all` flag to model download CLI Signed-off-by: Panos Vagenas --------- Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> Signed-off-by: Panos Vagenas --- docling/cli/models.py | 21 +++++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/docling/cli/models.py b/docling/cli/models.py index 50038adf..cc4a43ac 100644 --- a/docling/cli/models.py +++ b/docling/cli/models.py @@ -53,18 +53,27 @@ def download( ..., "-o", "--output-dir", - help="The directory where all the models are downloaded.", + help="The directory where to download the models.", ), ] = (settings.cache_dir / "models"), force: Annotated[ - bool, typer.Option(..., help="If true, the download will be forced") + bool, typer.Option(..., help="If true, the download will be forced.") ] = False, models: Annotated[ Optional[list[_AvailableModels]], typer.Argument( - help=f"Models to download (default behavior: all will be downloaded)", + help=f"Models to download (default behavior: a predefined set of models will be downloaded).", ), ] = None, + all: Annotated[ + bool, + typer.Option( + ..., + "--all", + help="If true, all available models will be downloaded (mutually exclusive with passing specific models).", + show_default=True, + ), + ] = False, quiet: Annotated[ bool, typer.Option( @@ -75,6 +84,10 @@ def download( ), ] = False, ): + if models and all: + raise typer.BadParameter( + "Cannot simultaneously set 'all' parameter and specify models to download." + ) if not quiet: FORMAT = "%(message)s" logging.basicConfig( @@ -83,7 +96,7 @@ def download( datefmt="[%X]", handlers=[RichHandler(show_level=False, show_time=False, markup=True)], ) - to_download = models or _default_models + to_download = models or ([m for m in _AvailableModels] if all else _default_models) output_dir = download_models( output_dir=output_dir, force=force, From 3c9fe76b706b7714b25d49cb09050c42e3b8c849 Mon Sep 17 00:00:00 2001 From: Christoph Auer <60343111+cau-git@users.noreply.github.com> Date: Wed, 26 Feb 2025 14:43:26 +0100 Subject: [PATCH 4/7] feat: [Experimental] Introduce VLM pipeline using HF AutoModelForVision2Seq, featuring SmolDocling model (#1054) * Skeleton for SmolDocling model and VLM Pipeline Signed-off-by: Christoph Auer Signed-off-by: Maksym Lysak * wip smolDocling inference and vlm pipeline Signed-off-by: Maksym Lysak * WIP, first working code for inference of SmolDocling, and vlm pipeline assembly code, example included. Signed-off-by: Maksym Lysak * Fixes to preserve page image and demo export to html Signed-off-by: Maksym Lysak * Enabled figure support in vlm_pipeline Signed-off-by: Maksym Lysak * Fix for table span compute in vlm_pipeline Signed-off-by: Maksym Lysak * Properly propagating image data per page, together with predicted tags in VLM pipeline. This enables correct figure extraction and page numbers in provenances Signed-off-by: Maksym Lysak * Cleaned up logs, added pages to vlm_pipeline, basic timing per page measurement in smol_docling models Signed-off-by: Maksym Lysak * Replaced hardcoded otsl tokens with the ones from docling-core tokens.py enum Signed-off-by: Maksym Lysak * Added tokens/sec measurement, improved example Signed-off-by: Maksym Lysak * Added capability for vlm_pipeline to grab text from preconfigured backend Signed-off-by: Maksym Lysak * Exposed "force_backend_text" as pipeline parameter Signed-off-by: Maksym Lysak * Flipped keep_backend to True for vlm_pipeline assembly to work Signed-off-by: Maksym Lysak * Updated vlm pipeline assembly and smol docling model code to support updated doctags Signed-off-by: Maksym Lysak * Fixing doctags starting tag, that broke elements on first line during assembly Signed-off-by: Maksym Lysak * Introduced SmolDoclingOptions to configure model parameters (such as query and artifacts path) via client code, see example in minimal_smol_docling. Provisioning for other potential vlm all-in-one models. Signed-off-by: Maksym Lysak * Moved artifacts_path for SmolDocling into vlm_options instead of global pipeline option Signed-off-by: Maksym Lysak * New assembly code for latest model revision, updated prompt and parsing of doctags, updated logging Signed-off-by: Maksym Lysak * Updated example of Smol Docling usage Signed-off-by: Maksym Lysak * Added captions for the images for SmolDocling assembly code, improved provenance definition for all elements Signed-off-by: Maksym Lysak * Update minimal smoldocling example Signed-off-by: Christoph Auer * Fix repo id Signed-off-by: Christoph Auer * Cleaned up unnecessary logging Signed-off-by: Maksym Lysak * More elegant solution in removing the input prompt Signed-off-by: Maksym Lysak * removed minimal_smol_docling example from CI checks Signed-off-by: Maksym Lysak * Removed special html code wrapping when exporting to docling document, cleaned up comments Signed-off-by: Maksym Lysak * Addressing PR comments, added enabled property to SmolDocling, and related VLM pipeline option, few other minor things Signed-off-by: Maksym Lysak * Moved keep_backend = True to vlm pipeline Signed-off-by: Maksym Lysak * removed pipeline_options.generate_table_images from vlm_pipeline (deprecated in the pipelines) Signed-off-by: Maksym Lysak * Added example on how to get original predicted doctags in minimal_smol_docling Signed-off-by: Maksym Lysak * removing changes from base_pipeline Signed-off-by: Maksym Lysak * Replaced remaining strings to appropriate enums Signed-off-by: Maksym Lysak * Updated poetry.lock Signed-off-by: Maksym Lysak * re-built poetry.lock Signed-off-by: Maksym Lysak * Generalize and refactor VLM pipeline and models Signed-off-by: Christoph Auer * Rename example Signed-off-by: Christoph Auer * Move imports Signed-off-by: Christoph Auer * Expose control over using flash_attention_2 Signed-off-by: Christoph Auer * Fix VLM example exclusion in CI Signed-off-by: Christoph Auer * Add back device_map and accelerate Signed-off-by: Christoph Auer * Make drawing code resilient against bad bboxes Signed-off-by: Christoph Auer * chore: clean up code and comments Signed-off-by: Christoph Auer * chore: more cleanup Signed-off-by: Christoph Auer * chore: fix leftover .to(device) Signed-off-by: Christoph Auer * fix: add proper table provenance Signed-off-by: Christoph Auer --------- Signed-off-by: Christoph Auer Signed-off-by: Maksym Lysak Co-authored-by: Maksym Lysak --- .github/workflows/checks.yml | 2 +- docling/datamodel/base_models.py | 5 + docling/datamodel/pipeline_options.py | 63 ++- docling/models/hf_vlm_model.py | 180 +++++++ docling/pipeline/vlm_pipeline.py | 534 ++++++++++++++++++++ docling/utils/visualization.py | 5 + docs/examples/minimal_vlm_pipeline.py | 96 ++++ poetry.lock | 673 ++++++++++++++------------ pyproject.toml | 6 +- 9 files changed, 1248 insertions(+), 316 deletions(-) create mode 100644 docling/models/hf_vlm_model.py create mode 100644 docling/pipeline/vlm_pipeline.py create mode 100644 docs/examples/minimal_vlm_pipeline.py diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml index 89bcfd79..1c322d9a 100644 --- a/.github/workflows/checks.yml +++ b/.github/workflows/checks.yml @@ -28,7 +28,7 @@ jobs: run: | for file in docs/examples/*.py; do # Skip batch_convert.py - if [[ "$(basename "$file")" =~ ^(batch_convert|minimal|export_multimodal|custom_convert|develop_picture_enrichment|rapidocr_with_custom_models|offline_convert|pictures_description|pictures_description_api).py ]]; then + if [[ "$(basename "$file")" =~ ^(batch_convert|minimal_vlm_pipeline|minimal|export_multimodal|custom_convert|develop_picture_enrichment|rapidocr_with_custom_models|offline_convert|pictures_description|pictures_description_api).py ]]; then echo "Skipping $file" continue fi diff --git a/docling/datamodel/base_models.py b/docling/datamodel/base_models.py index da512aa1..3297c9a5 100644 --- a/docling/datamodel/base_models.py +++ b/docling/datamodel/base_models.py @@ -154,6 +154,10 @@ class LayoutPrediction(BaseModel): clusters: List[Cluster] = [] +class VlmPrediction(BaseModel): + text: str = "" + + class ContainerElement( BasePageElement ): # Used for Form and Key-Value-Regions, only for typing. @@ -197,6 +201,7 @@ class PagePredictions(BaseModel): tablestructure: Optional[TableStructurePrediction] = None figures_classification: Optional[FigureClassificationPrediction] = None equations_prediction: Optional[EquationPrediction] = None + vlm_response: Optional[VlmPrediction] = None PageElement = Union[TextElement, Table, FigureElement, ContainerElement] diff --git a/docling/datamodel/pipeline_options.py b/docling/datamodel/pipeline_options.py index d317e7d9..3a55ecfc 100644 --- a/docling/datamodel/pipeline_options.py +++ b/docling/datamodel/pipeline_options.py @@ -41,6 +41,7 @@ class AcceleratorOptions(BaseSettings): num_threads: int = 4 device: Union[str, AcceleratorDevice] = "auto" + cuda_use_flash_attention2: bool = False @field_validator("device") def validate_device(cls, value): @@ -254,6 +255,45 @@ granite_picture_description = PictureDescriptionVlmOptions( ) +class BaseVlmOptions(BaseModel): + kind: str + prompt: str + + +class ResponseFormat(str, Enum): + DOCTAGS = "doctags" + MARKDOWN = "markdown" + + +class HuggingFaceVlmOptions(BaseVlmOptions): + kind: Literal["hf_model_options"] = "hf_model_options" + + repo_id: str + load_in_8bit: bool = True + llm_int8_threshold: float = 6.0 + quantized: bool = False + + response_format: ResponseFormat + + @property + def repo_cache_folder(self) -> str: + return self.repo_id.replace("/", "--") + + +smoldocling_vlm_conversion_options = HuggingFaceVlmOptions( + repo_id="ds4sd/SmolDocling-256M-preview", + prompt="Convert this page to docling.", + response_format=ResponseFormat.DOCTAGS, +) + +granite_vision_vlm_conversion_options = HuggingFaceVlmOptions( + repo_id="ibm-granite/granite-vision-3.1-2b-preview", + # prompt="OCR the full page to markdown.", + prompt="OCR this image.", + response_format=ResponseFormat.MARKDOWN, +) + + # Define an enum for the backend options class PdfBackend(str, Enum): """Enum of valid PDF backends.""" @@ -285,7 +325,24 @@ class PipelineOptions(BaseModel): enable_remote_services: bool = False -class PdfPipelineOptions(PipelineOptions): +class PaginatedPipelineOptions(PipelineOptions): + images_scale: float = 1.0 + generate_page_images: bool = False + generate_picture_images: bool = False + + +class VlmPipelineOptions(PaginatedPipelineOptions): + artifacts_path: Optional[Union[Path, str]] = None + + generate_page_images: bool = True + force_backend_text: bool = ( + False # (To be used with vlms, or other generative models) + ) + # If True, text from backend will be used instead of generated text + vlm_options: Union[HuggingFaceVlmOptions] = smoldocling_vlm_conversion_options + + +class PdfPipelineOptions(PaginatedPipelineOptions): """Options for the PDF pipeline.""" artifacts_path: Optional[Union[Path, str]] = None @@ -295,6 +352,10 @@ class PdfPipelineOptions(PipelineOptions): do_formula_enrichment: bool = False # True: perform formula OCR, return Latex code do_picture_classification: bool = False # True: classify pictures in documents do_picture_description: bool = False # True: run describe pictures in documents + force_backend_text: bool = ( + False # (To be used with vlms, or other generative models) + ) + # If True, text from backend will be used instead of generated text table_structure_options: TableStructureOptions = TableStructureOptions() ocr_options: Union[ diff --git a/docling/models/hf_vlm_model.py b/docling/models/hf_vlm_model.py new file mode 100644 index 00000000..2acbe290 --- /dev/null +++ b/docling/models/hf_vlm_model.py @@ -0,0 +1,180 @@ +import logging +import time +from pathlib import Path +from typing import Iterable, List, Optional + +from docling.datamodel.base_models import Page, VlmPrediction +from docling.datamodel.document import ConversionResult +from docling.datamodel.pipeline_options import ( + AcceleratorDevice, + AcceleratorOptions, + HuggingFaceVlmOptions, +) +from docling.datamodel.settings import settings +from docling.models.base_model import BasePageModel +from docling.utils.accelerator_utils import decide_device +from docling.utils.profiling import TimeRecorder + +_log = logging.getLogger(__name__) + + +class HuggingFaceVlmModel(BasePageModel): + + def __init__( + self, + enabled: bool, + artifacts_path: Optional[Path], + accelerator_options: AcceleratorOptions, + vlm_options: HuggingFaceVlmOptions, + ): + self.enabled = enabled + + self.vlm_options = vlm_options + + if self.enabled: + import torch + from transformers import ( # type: ignore + AutoModelForVision2Seq, + AutoProcessor, + BitsAndBytesConfig, + ) + + device = decide_device(accelerator_options.device) + self.device = device + + _log.debug("Available device for HuggingFace VLM: {}".format(device)) + + repo_cache_folder = vlm_options.repo_id.replace("/", "--") + + # PARAMETERS: + if artifacts_path is None: + artifacts_path = self.download_models(self.vlm_options.repo_id) + elif (artifacts_path / repo_cache_folder).exists(): + artifacts_path = artifacts_path / repo_cache_folder + + self.param_question = vlm_options.prompt # "Perform Layout Analysis." + self.param_quantization_config = BitsAndBytesConfig( + load_in_8bit=vlm_options.load_in_8bit, # True, + llm_int8_threshold=vlm_options.llm_int8_threshold, # 6.0 + ) + self.param_quantized = vlm_options.quantized # False + + self.processor = AutoProcessor.from_pretrained(artifacts_path) + if not self.param_quantized: + self.vlm_model = AutoModelForVision2Seq.from_pretrained( + artifacts_path, + device_map=device, + torch_dtype=torch.bfloat16, + _attn_implementation=( + "flash_attention_2" + if self.device.startswith("cuda") + and accelerator_options.cuda_use_flash_attention2 + else "eager" + ), + ) # .to(self.device) + + else: + self.vlm_model = AutoModelForVision2Seq.from_pretrained( + artifacts_path, + device_map=device, + torch_dtype="auto", + quantization_config=self.param_quantization_config, + _attn_implementation=( + "flash_attention_2" + if self.device.startswith("cuda") + and accelerator_options.cuda_use_flash_attention2 + else "eager" + ), + ) # .to(self.device) + + @staticmethod + def download_models( + repo_id: str, + local_dir: Optional[Path] = None, + force: bool = False, + progress: bool = False, + ) -> Path: + from huggingface_hub import snapshot_download + from huggingface_hub.utils import disable_progress_bars + + if not progress: + disable_progress_bars() + download_path = snapshot_download( + repo_id=repo_id, + force_download=force, + local_dir=local_dir, + # revision="v0.0.1", + ) + + return Path(download_path) + + def __call__( + self, conv_res: ConversionResult, page_batch: Iterable[Page] + ) -> Iterable[Page]: + for page in page_batch: + assert page._backend is not None + if not page._backend.is_valid(): + yield page + else: + with TimeRecorder(conv_res, "vlm"): + assert page.size is not None + + hi_res_image = page.get_image(scale=2.0) # 144dpi + # hi_res_image = page.get_image(scale=1.0) # 72dpi + + if hi_res_image is not None: + im_width, im_height = hi_res_image.size + + # populate page_tags with predicted doc tags + page_tags = "" + + if hi_res_image: + if hi_res_image.mode != "RGB": + hi_res_image = hi_res_image.convert("RGB") + + messages = [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "This is a page from a document.", + }, + {"type": "image"}, + {"type": "text", "text": self.param_question}, + ], + } + ] + prompt = self.processor.apply_chat_template( + messages, add_generation_prompt=False + ) + inputs = self.processor( + text=prompt, images=[hi_res_image], return_tensors="pt" + ) + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + start_time = time.time() + # Call model to generate: + generated_ids = self.vlm_model.generate( + **inputs, max_new_tokens=4096, use_cache=True + ) + + generation_time = time.time() - start_time + generated_texts = self.processor.batch_decode( + generated_ids[:, inputs["input_ids"].shape[1] :], + skip_special_tokens=False, + )[0] + + num_tokens = len(generated_ids[0]) + page_tags = generated_texts + + # inference_time = time.time() - start_time + # tokens_per_second = num_tokens / generation_time + # print("") + # print(f"Page Inference Time: {inference_time:.2f} seconds") + # print(f"Total tokens on page: {num_tokens:.2f}") + # print(f"Tokens/sec: {tokens_per_second:.2f}") + # print("") + page.predictions.vlm_response = VlmPrediction(text=page_tags) + + yield page diff --git a/docling/pipeline/vlm_pipeline.py b/docling/pipeline/vlm_pipeline.py new file mode 100644 index 00000000..c6920b65 --- /dev/null +++ b/docling/pipeline/vlm_pipeline.py @@ -0,0 +1,534 @@ +import itertools +import logging +import re +import warnings +from io import BytesIO + +# from io import BytesIO +from pathlib import Path +from typing import Optional + +from docling_core.types import DoclingDocument +from docling_core.types.doc import ( + BoundingBox, + DocItem, + DocItemLabel, + DoclingDocument, + GroupLabel, + ImageRef, + ImageRefMode, + PictureItem, + ProvenanceItem, + Size, + TableCell, + TableData, + TableItem, +) +from docling_core.types.doc.tokens import DocumentToken, TableToken + +from docling.backend.abstract_backend import AbstractDocumentBackend +from docling.backend.md_backend import MarkdownDocumentBackend +from docling.backend.pdf_backend import PdfDocumentBackend +from docling.datamodel.base_models import InputFormat, Page +from docling.datamodel.document import ConversionResult, InputDocument +from docling.datamodel.pipeline_options import ( + PdfPipelineOptions, + ResponseFormat, + VlmPipelineOptions, +) +from docling.datamodel.settings import settings +from docling.models.hf_vlm_model import HuggingFaceVlmModel +from docling.pipeline.base_pipeline import PaginatedPipeline +from docling.utils.profiling import ProfilingScope, TimeRecorder + +_log = logging.getLogger(__name__) + + +class VlmPipeline(PaginatedPipeline): + + def __init__(self, pipeline_options: VlmPipelineOptions): + super().__init__(pipeline_options) + self.keep_backend = True + + warnings.warn( + "The VlmPipeline is currently experimental and may change in upcoming versions without notice.", + category=UserWarning, + stacklevel=2, + ) + + self.pipeline_options: VlmPipelineOptions + + artifacts_path: Optional[Path] = None + if pipeline_options.artifacts_path is not None: + artifacts_path = Path(pipeline_options.artifacts_path).expanduser() + elif settings.artifacts_path is not None: + artifacts_path = Path(settings.artifacts_path).expanduser() + + if artifacts_path is not None and not artifacts_path.is_dir(): + raise RuntimeError( + f"The value of {artifacts_path=} is not valid. " + "When defined, it must point to a folder containing all models required by the pipeline." + ) + + # force_backend_text = False - use text that is coming from VLM response + # force_backend_text = True - get text from backend using bounding boxes predicted by SmolDocling doctags + self.force_backend_text = ( + pipeline_options.force_backend_text + and pipeline_options.vlm_options.response_format == ResponseFormat.DOCTAGS + ) + + self.keep_images = self.pipeline_options.generate_page_images + + self.build_pipe = [ + HuggingFaceVlmModel( + enabled=True, # must be always enabled for this pipeline to make sense. + artifacts_path=artifacts_path, + accelerator_options=pipeline_options.accelerator_options, + vlm_options=self.pipeline_options.vlm_options, + ), + ] + + self.enrichment_pipe = [ + # Other models working on `NodeItem` elements in the DoclingDocument + ] + + def initialize_page(self, conv_res: ConversionResult, page: Page) -> Page: + with TimeRecorder(conv_res, "page_init"): + page._backend = conv_res.input._backend.load_page(page.page_no) # type: ignore + if page._backend is not None and page._backend.is_valid(): + page.size = page._backend.get_size() + + return page + + def _assemble_document(self, conv_res: ConversionResult) -> ConversionResult: + with TimeRecorder(conv_res, "doc_assemble", scope=ProfilingScope.DOCUMENT): + + if ( + self.pipeline_options.vlm_options.response_format + == ResponseFormat.DOCTAGS + ): + conv_res.document = self._turn_tags_into_doc(conv_res.pages) + elif ( + self.pipeline_options.vlm_options.response_format + == ResponseFormat.MARKDOWN + ): + conv_res.document = self._turn_md_into_doc(conv_res) + + else: + raise RuntimeError( + f"Unsupported VLM response format {self.pipeline_options.vlm_options.response_format}" + ) + + # Generate images of the requested element types + if self.pipeline_options.generate_picture_images: + scale = self.pipeline_options.images_scale + for element, _level in conv_res.document.iterate_items(): + if not isinstance(element, DocItem) or len(element.prov) == 0: + continue + if ( + isinstance(element, PictureItem) + and self.pipeline_options.generate_picture_images + ): + page_ix = element.prov[0].page_no - 1 + page = conv_res.pages[page_ix] + assert page.size is not None + assert page.image is not None + + crop_bbox = ( + element.prov[0] + .bbox.scaled(scale=scale) + .to_top_left_origin(page_height=page.size.height * scale) + ) + + cropped_im = page.image.crop(crop_bbox.as_tuple()) + element.image = ImageRef.from_pil( + cropped_im, dpi=int(72 * scale) + ) + + return conv_res + + def _turn_md_into_doc(self, conv_res): + predicted_text = "" + for pg_idx, page in enumerate(conv_res.pages): + if page.predictions.vlm_response: + predicted_text += page.predictions.vlm_response.text + "\n\n" + response_bytes = BytesIO(predicted_text.encode("utf8")) + out_doc = InputDocument( + path_or_stream=response_bytes, + filename=conv_res.input.file.name, + format=InputFormat.MD, + backend=MarkdownDocumentBackend, + ) + backend = MarkdownDocumentBackend( + in_doc=out_doc, + path_or_stream=response_bytes, + ) + return backend.convert() + + def _turn_tags_into_doc(self, pages: list[Page]) -> DoclingDocument: + ############################################### + # Tag definitions and color mappings + ############################################### + + # Maps the recognized tag to a Docling label. + # Code items will be given DocItemLabel.CODE + tag_to_doclabel = { + "title": DocItemLabel.TITLE, + "document_index": DocItemLabel.DOCUMENT_INDEX, + "otsl": DocItemLabel.TABLE, + "section_header_level_1": DocItemLabel.SECTION_HEADER, + "checkbox_selected": DocItemLabel.CHECKBOX_SELECTED, + "checkbox_unselected": DocItemLabel.CHECKBOX_UNSELECTED, + "text": DocItemLabel.TEXT, + "page_header": DocItemLabel.PAGE_HEADER, + "page_footer": DocItemLabel.PAGE_FOOTER, + "formula": DocItemLabel.FORMULA, + "caption": DocItemLabel.CAPTION, + "picture": DocItemLabel.PICTURE, + "list_item": DocItemLabel.LIST_ITEM, + "footnote": DocItemLabel.FOOTNOTE, + "code": DocItemLabel.CODE, + } + + # Maps each tag to an associated bounding box color. + tag_to_color = { + "title": "blue", + "document_index": "darkblue", + "otsl": "green", + "section_header_level_1": "purple", + "checkbox_selected": "black", + "checkbox_unselected": "gray", + "text": "red", + "page_header": "orange", + "page_footer": "cyan", + "formula": "pink", + "caption": "magenta", + "picture": "yellow", + "list_item": "brown", + "footnote": "darkred", + "code": "lightblue", + } + + def extract_bounding_box(text_chunk: str) -> Optional[BoundingBox]: + """Extracts bounding box coords from the chunk, normalized by / 500.""" + coords = re.findall(r"", text_chunk) + if len(coords) == 4: + l, t, r, b = map(float, coords) + return BoundingBox(l=l / 500, t=t / 500, r=r / 500, b=b / 500) + return None + + def extract_inner_text(text_chunk: str) -> str: + """Strips all <...> tags inside the chunk to get the raw text content.""" + return re.sub(r"<.*?>", "", text_chunk, flags=re.DOTALL).strip() + + def extract_text_from_backend(page: Page, bbox: BoundingBox | None) -> str: + # Convert bounding box normalized to 0-100 into page coordinates for cropping + text = "" + if bbox: + if page.size: + bbox.l = bbox.l * page.size.width + bbox.t = bbox.t * page.size.height + bbox.r = bbox.r * page.size.width + bbox.b = bbox.b * page.size.height + if page._backend: + text = page._backend.get_text_in_rect(bbox) + return text + + def otsl_parse_texts(texts, tokens): + split_word = TableToken.OTSL_NL.value + split_row_tokens = [ + list(y) + for x, y in itertools.groupby(tokens, lambda z: z == split_word) + if not x + ] + table_cells = [] + r_idx = 0 + c_idx = 0 + + def count_right(tokens, c_idx, r_idx, which_tokens): + span = 0 + c_idx_iter = c_idx + while tokens[r_idx][c_idx_iter] in which_tokens: + c_idx_iter += 1 + span += 1 + if c_idx_iter >= len(tokens[r_idx]): + return span + return span + + def count_down(tokens, c_idx, r_idx, which_tokens): + span = 0 + r_idx_iter = r_idx + while tokens[r_idx_iter][c_idx] in which_tokens: + r_idx_iter += 1 + span += 1 + if r_idx_iter >= len(tokens): + return span + return span + + for i, text in enumerate(texts): + cell_text = "" + if text in [ + TableToken.OTSL_FCEL.value, + TableToken.OTSL_ECEL.value, + TableToken.OTSL_CHED.value, + TableToken.OTSL_RHED.value, + TableToken.OTSL_SROW.value, + ]: + row_span = 1 + col_span = 1 + right_offset = 1 + if text != TableToken.OTSL_ECEL.value: + cell_text = texts[i + 1] + right_offset = 2 + + # Check next element(s) for lcel / ucel / xcel, set properly row_span, col_span + next_right_cell = "" + if i + right_offset < len(texts): + next_right_cell = texts[i + right_offset] + + next_bottom_cell = "" + if r_idx + 1 < len(split_row_tokens): + if c_idx < len(split_row_tokens[r_idx + 1]): + next_bottom_cell = split_row_tokens[r_idx + 1][c_idx] + + if next_right_cell in [ + TableToken.OTSL_LCEL.value, + TableToken.OTSL_XCEL.value, + ]: + # we have horisontal spanning cell or 2d spanning cell + col_span += count_right( + split_row_tokens, + c_idx + 1, + r_idx, + [TableToken.OTSL_LCEL.value, TableToken.OTSL_XCEL.value], + ) + if next_bottom_cell in [ + TableToken.OTSL_UCEL.value, + TableToken.OTSL_XCEL.value, + ]: + # we have a vertical spanning cell or 2d spanning cell + row_span += count_down( + split_row_tokens, + c_idx, + r_idx + 1, + [TableToken.OTSL_UCEL.value, TableToken.OTSL_XCEL.value], + ) + + table_cells.append( + TableCell( + text=cell_text.strip(), + row_span=row_span, + col_span=col_span, + start_row_offset_idx=r_idx, + end_row_offset_idx=r_idx + row_span, + start_col_offset_idx=c_idx, + end_col_offset_idx=c_idx + col_span, + ) + ) + if text in [ + TableToken.OTSL_FCEL.value, + TableToken.OTSL_ECEL.value, + TableToken.OTSL_CHED.value, + TableToken.OTSL_RHED.value, + TableToken.OTSL_SROW.value, + TableToken.OTSL_LCEL.value, + TableToken.OTSL_UCEL.value, + TableToken.OTSL_XCEL.value, + ]: + c_idx += 1 + if text == TableToken.OTSL_NL.value: + r_idx += 1 + c_idx = 0 + return table_cells, split_row_tokens + + def otsl_extract_tokens_and_text(s: str): + # Pattern to match anything enclosed by < > (including the angle brackets themselves) + pattern = r"(<[^>]+>)" + # Find all tokens (e.g. "", "", etc.) + tokens = re.findall(pattern, s) + # Remove any tokens that start with "", + rf"", + ] + ) + ] + # Split the string by those tokens to get the in-between text + text_parts = re.split(pattern, s) + text_parts = [ + token + for token in text_parts + if not ( + token.startswith(rf"<{DocumentToken.LOC.value}") + or token + in [ + rf"<{DocumentToken.OTSL.value}>", + rf"", + ] + ) + ] + # Remove any empty or purely whitespace strings from text_parts + text_parts = [part for part in text_parts if part.strip()] + + return tokens, text_parts + + def parse_table_content(otsl_content: str) -> TableData: + tokens, mixed_texts = otsl_extract_tokens_and_text(otsl_content) + table_cells, split_row_tokens = otsl_parse_texts(mixed_texts, tokens) + + return TableData( + num_rows=len(split_row_tokens), + num_cols=( + max(len(row) for row in split_row_tokens) if split_row_tokens else 0 + ), + table_cells=table_cells, + ) + + doc = DoclingDocument(name="Document") + for pg_idx, page in enumerate(pages): + xml_content = "" + predicted_text = "" + if page.predictions.vlm_response: + predicted_text = page.predictions.vlm_response.text + image = page.image + + page_no = pg_idx + 1 + bounding_boxes = [] + + if page.size: + pg_width = page.size.width + pg_height = page.size.height + size = Size(width=pg_width, height=pg_height) + parent_page = doc.add_page(page_no=page_no, size=size) + + """ + 1. Finds all ... blocks in the entire string (multi-line friendly) in the order they appear. + 2. For each chunk, extracts bounding box (if any) and inner text. + 3. Adds the item to a DoclingDocument structure with the right label. + 4. Tracks bounding boxes + color in a separate list for later visualization. + """ + + # Regex for all recognized tags + tag_pattern = ( + rf"<(?P{DocItemLabel.TITLE}|{DocItemLabel.DOCUMENT_INDEX}|" + rf"{DocItemLabel.CHECKBOX_UNSELECTED}|{DocItemLabel.CHECKBOX_SELECTED}|" + rf"{DocItemLabel.TEXT}|{DocItemLabel.PAGE_HEADER}|" + rf"{DocItemLabel.PAGE_FOOTER}|{DocItemLabel.FORMULA}|" + rf"{DocItemLabel.CAPTION}|{DocItemLabel.PICTURE}|" + rf"{DocItemLabel.LIST_ITEM}|{DocItemLabel.FOOTNOTE}|{DocItemLabel.CODE}|" + rf"{DocItemLabel.SECTION_HEADER}_level_1|{DocumentToken.OTSL.value})>.*?" + ) + + # DocumentToken.OTSL + pattern = re.compile(tag_pattern, re.DOTALL) + + # Go through each match in order + for match in pattern.finditer(predicted_text): + full_chunk = match.group(0) + tag_name = match.group("tag") + + bbox = extract_bounding_box(full_chunk) + doc_label = tag_to_doclabel.get(tag_name, DocItemLabel.PARAGRAPH) + color = tag_to_color.get(tag_name, "white") + + # Store bounding box + color + if bbox: + bounding_boxes.append((bbox, color)) + + if tag_name == DocumentToken.OTSL.value: + table_data = parse_table_content(full_chunk) + bbox = extract_bounding_box(full_chunk) + + if bbox: + prov = ProvenanceItem( + bbox=bbox.resize_by_scale(pg_width, pg_height), + charspan=(0, 0), + page_no=page_no, + ) + doc.add_table(data=table_data, prov=prov) + else: + doc.add_table(data=table_data) + + elif tag_name == DocItemLabel.PICTURE: + text_caption_content = extract_inner_text(full_chunk) + if image: + if bbox: + im_width, im_height = image.size + + crop_box = ( + int(bbox.l * im_width), + int(bbox.t * im_height), + int(bbox.r * im_width), + int(bbox.b * im_height), + ) + cropped_image = image.crop(crop_box) + pic = doc.add_picture( + parent=None, + image=ImageRef.from_pil(image=cropped_image, dpi=72), + prov=( + ProvenanceItem( + bbox=bbox.resize_by_scale(pg_width, pg_height), + charspan=(0, 0), + page_no=page_no, + ) + ), + ) + # If there is a caption to an image, add it as well + if len(text_caption_content) > 0: + caption_item = doc.add_text( + label=DocItemLabel.CAPTION, + text=text_caption_content, + parent=None, + ) + pic.captions.append(caption_item.get_ref()) + else: + if bbox: + # In case we don't have access to an binary of an image + doc.add_picture( + parent=None, + prov=ProvenanceItem( + bbox=bbox, charspan=(0, 0), page_no=page_no + ), + ) + # If there is a caption to an image, add it as well + if len(text_caption_content) > 0: + caption_item = doc.add_text( + label=DocItemLabel.CAPTION, + text=text_caption_content, + parent=None, + ) + pic.captions.append(caption_item.get_ref()) + else: + # For everything else, treat as text + if self.force_backend_text: + text_content = extract_text_from_backend(page, bbox) + else: + text_content = extract_inner_text(full_chunk) + doc.add_text( + label=doc_label, + text=text_content, + prov=( + ProvenanceItem( + bbox=bbox.resize_by_scale(pg_width, pg_height), + charspan=(0, len(text_content)), + page_no=page_no, + ) + if bbox + else None + ), + ) + return doc + + @classmethod + def get_default_options(cls) -> VlmPipelineOptions: + return VlmPipelineOptions() + + @classmethod + def is_backend_supported(cls, backend: AbstractDocumentBackend): + return isinstance(backend, PdfDocumentBackend) diff --git a/docling/utils/visualization.py b/docling/utils/visualization.py index 465b7749..e7ea24a5 100644 --- a/docling/utils/visualization.py +++ b/docling/utils/visualization.py @@ -43,6 +43,11 @@ def draw_clusters( y0 *= scale_x y1 *= scale_y + if y1 <= y0: + y1, y0 = y0, y1 + if x1 <= x0: + x1, x0 = x0, x1 + cluster_fill_color = (*list(DocItemLabel.get_color(c.label)), 70) cluster_outline_color = ( *list(DocItemLabel.get_color(c.label)), diff --git a/docs/examples/minimal_vlm_pipeline.py b/docs/examples/minimal_vlm_pipeline.py new file mode 100644 index 00000000..948ecc64 --- /dev/null +++ b/docs/examples/minimal_vlm_pipeline.py @@ -0,0 +1,96 @@ +import json +import time +from pathlib import Path + +import yaml + +from docling.datamodel.base_models import InputFormat +from docling.datamodel.pipeline_options import ( + AcceleratorDevice, + VlmPipelineOptions, + granite_vision_vlm_conversion_options, + smoldocling_vlm_conversion_options, +) +from docling.datamodel.settings import settings +from docling.document_converter import DocumentConverter, PdfFormatOption +from docling.pipeline.vlm_pipeline import VlmPipeline + +sources = [ + "tests/data/2305.03393v1-pg9-img.png", +] + +## Use experimental VlmPipeline +pipeline_options = VlmPipelineOptions() +# If force_backend_text = True, text from backend will be used instead of generated text +pipeline_options.force_backend_text = False + +## On GPU systems, enable flash_attention_2 with CUDA: +# pipeline_options.accelerator_options.device = AcceleratorDevice.CUDA +# pipeline_options.accelerator_options.cuda_use_flash_attention2 = True + +## Pick a VLM model. We choose SmolDocling-256M by default +pipeline_options.vlm_options = smoldocling_vlm_conversion_options + +## Alternative VLM models: +# pipeline_options.vlm_options = granite_vision_vlm_conversion_options + +from docling_core.types.doc import DocItemLabel, ImageRefMode +from docling_core.types.doc.document import DEFAULT_EXPORT_LABELS + +## Set up pipeline for PDF or image inputs +converter = DocumentConverter( + format_options={ + InputFormat.PDF: PdfFormatOption( + pipeline_cls=VlmPipeline, + pipeline_options=pipeline_options, + ), + InputFormat.IMAGE: PdfFormatOption( + pipeline_cls=VlmPipeline, + pipeline_options=pipeline_options, + ), + } +) + +out_path = Path("scratch") +out_path.mkdir(parents=True, exist_ok=True) + +for source in sources: + start_time = time.time() + print("================================================") + print("Processing... {}".format(source)) + print("================================================") + print("") + + res = converter.convert(source) + + print("------------------------------------------------") + print("MD:") + print("------------------------------------------------") + print("") + print(res.document.export_to_markdown()) + + for page in res.pages: + print("") + print("Predicted page in DOCTAGS:") + print(page.predictions.vlm_response.text) + + res.document.save_as_html( + filename=Path("{}/{}.html".format(out_path, res.input.file.stem)), + image_mode=ImageRefMode.REFERENCED, + labels=[*DEFAULT_EXPORT_LABELS, DocItemLabel.FOOTNOTE], + ) + + with (out_path / f"{res.input.file.stem}.json").open("w") as fp: + fp.write(json.dumps(res.document.export_to_dict())) + + pg_num = res.document.num_pages() + + print("") + inference_time = time.time() - start_time + print( + f"Total document prediction time: {inference_time:.2f} seconds, pages: {pg_num}" + ) + +print("================================================") +print("done!") +print("================================================") diff --git a/poetry.lock b/poetry.lock index 1f35c6d7..374dfe0d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,35 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. + +[[package]] +name = "accelerate" +version = "1.4.0" +description = "Accelerate" +optional = true +python-versions = ">=3.9.0" +files = [ + {file = "accelerate-1.4.0-py3-none-any.whl", hash = "sha256:f6e1e7dfaf9d799a20a1dc45efbf4b1546163eac133faa5acd0d89177c896e55"}, + {file = "accelerate-1.4.0.tar.gz", hash = "sha256:37d413e1b64cb8681ccd2908ae211cf73e13e6e636a2f598a96eccaa538773a5"}, +] + +[package.dependencies] +huggingface-hub = ">=0.21.0" +numpy = ">=1.17,<3.0.0" +packaging = ">=20.0" +psutil = "*" +pyyaml = "*" +safetensors = ">=0.4.3" +torch = ">=2.0.0" + +[package.extras] +deepspeed = ["deepspeed"] +dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", 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'3.10'" } ] + transformers = [ {markers = "sys_platform != 'darwin' or platform_machine != 'x86_64'", version = "^4.46.0", optional = true }, {markers = "sys_platform == 'darwin' and platform_machine == 'x86_64'", version = "~4.42.0", optional = true } ] +accelerate = [ + {markers = "sys_platform != 'darwin' or platform_machine != 'x86_64'", version = "^1.2.1", optional = true }, +] pillow = ">=10.0.0,<12.0.0" tqdm = "^4.65.0" @@ -124,7 +128,7 @@ torchvision = [ [tool.poetry.extras] tesserocr = ["tesserocr"] ocrmac = ["ocrmac"] -vlm = ["transformers"] +vlm = ["transformers", "accelerate"] rapidocr = ["rapidocr-onnxruntime", "onnxruntime"] [tool.poetry.scripts] From 37dd8c1cc7fc05095fe889eec78300647c946a42 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Wed, 26 Feb 2025 14:16:15 +0000 Subject: [PATCH 5/7] chore: bump version to 2.25.0 [skip ci] --- CHANGELOG.md | 16 ++++++++++++++++ pyproject.toml | 2 +- 2 files changed, 17 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ab946a87..500808ba 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,19 @@ +## [v2.25.0](https://github.com/DS4SD/docling/releases/tag/v2.25.0) - 2025-02-26 + +### Feature + +* [Experimental] Introduce VLM pipeline using HF AutoModelForVision2Seq, featuring SmolDocling model ([#1054](https://github.com/DS4SD/docling/issues/1054)) ([`3c9fe76`](https://github.com/DS4SD/docling/commit/3c9fe76b706b7714b25d49cb09050c42e3b8c849)) +* **cli:** Add option for downloading all models, refine help messages ([#1061](https://github.com/DS4SD/docling/issues/1061)) ([`ab683e4`](https://github.com/DS4SD/docling/commit/ab683e4fb6df4973d2efda04f00c269a2dc95f5b)) + +### Fix + +* Vlm using artifacts path ([#1057](https://github.com/DS4SD/docling/issues/1057)) ([`e197225`](https://github.com/DS4SD/docling/commit/e1972257399151503d60b4806976c8b9b6911aa8)) +* **html:** Parse text in div elements as TextItem ([#1041](https://github.com/DS4SD/docling/issues/1041)) ([`1b0ead6`](https://github.com/DS4SD/docling/commit/1b0ead69078030a0e4d25b51450ef2aa4a2e79fc)) + +### Documentation + +* Extend chunking docs, add FAQ on token limit ([#1053](https://github.com/DS4SD/docling/issues/1053)) ([`c84b973`](https://github.com/DS4SD/docling/commit/c84b973959a254db22ac9a7dc8810628e4808a2d)) + ## [v2.24.0](https://github.com/DS4SD/docling/releases/tag/v2.24.0) - 2025-02-20 ### Feature diff --git a/pyproject.toml b/pyproject.toml index 647d586f..afb98689 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "docling" -version = "2.24.0" # DO NOT EDIT, updated automatically +version = "2.25.0" # DO NOT EDIT, updated automatically description = "SDK and CLI for parsing PDF, DOCX, HTML, and more, to a unified document representation for powering downstream workflows such as gen AI applications." authors = ["Christoph Auer ", "Michele Dolfi ", "Maxim Lysak ", "Nikos Livathinos ", "Ahmed Nassar ", "Panos Vagenas ", "Peter Staar "] license = "MIT" From de7b963b09a34916f0a8d99649269aeb37db1408 Mon Sep 17 00:00:00 2001 From: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> Date: Thu, 27 Feb 2025 09:46:57 +0100 Subject: [PATCH 6/7] fix(html): use 'start' attribute when parsing ordered lists from HTML docs (#1062) * fix(html): use 'start' attribute in ordered lists When parsing ordered lists in HTML, take into account the 'start' attribute if it exists. Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> * chore(html): reduce verbosity in HTML backend Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> --------- Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> --- docling/backend/html_backend.py | 24 +++++++++++--- tests/test_backend_html.py | 58 ++++++++++++++++++++++++++++++++- 2 files changed, 76 insertions(+), 6 deletions(-) diff --git a/docling/backend/html_backend.py b/docling/backend/html_backend.py index 00ef05b4..f2320693 100644 --- a/docling/backend/html_backend.py +++ b/docling/backend/html_backend.py @@ -256,10 +256,16 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend): parent=self.parents[self.level], name="list", label=GroupLabel.LIST ) elif element.name == "ol": + start_attr = element.get("start") + start: int = ( + int(start_attr) + if isinstance(start_attr, str) and start_attr.isnumeric() + else 1 + ) # create a list group self.parents[self.level + 1] = doc.add_group( parent=self.parents[self.level], - name="ordered list", + name="ordered list" + (f" start {start}" if start != 1 else ""), label=GroupLabel.ORDERED_LIST, ) self.level += 1 @@ -270,15 +276,23 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend): self.level -= 1 def handle_list_item(self, element: Tag, doc: DoclingDocument) -> None: - """Handles listitem tags (li).""" + """Handles list item tags (li).""" nested_list = element.find(["ul", "ol"]) parent = self.parents[self.level] if parent is None: - _log.warning(f"list-item has no parent in DoclingDocument: {element}") + _log.debug(f"list-item has no parent in DoclingDocument: {element}") return parent_label: str = parent.label index_in_list = len(parent.children) + 1 + if ( + parent_label == GroupLabel.ORDERED_LIST + and isinstance(parent, GroupItem) + and parent.name + ): + start_in_list: str = parent.name.split(" ")[-1] + start: int = int(start_in_list) if start_in_list.isnumeric() else 1 + index_in_list += start - 1 if nested_list: # Text in list item can be hidden within hierarchy, hence @@ -324,13 +338,13 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend): parent=parent, ) else: - _log.warning(f"list-item has no text: {element}") + _log.debug(f"list-item has no text: {element}") @staticmethod def parse_table_data(element: Tag) -> Optional[TableData]: nested_tables = element.find("table") if nested_tables is not None: - _log.warning("Skipping nested table.") + _log.debug("Skipping nested table.") return None # Count the number of rows (number of elements) diff --git a/tests/test_backend_html.py b/tests/test_backend_html.py index 6c1db062..a04ae219 100644 --- a/tests/test_backend_html.py +++ b/tests/test_backend_html.py @@ -1,4 +1,4 @@ -import os +from io import BytesIO from pathlib import Path from docling.backend.html_backend import HTMLDocumentBackend @@ -41,6 +41,62 @@ def test_heading_levels(): assert found_lvl_2 and found_lvl_3 +def test_ordered_lists(): + test_set: list[tuple[bytes, str]] = [] + + test_set.append( + ( + b"
  1. 1st item
  2. 2nd item
", + "1. 1st item\n2. 2nd item", + ) + ) + test_set.append( + ( + b'
  1. 1st item
  2. 2nd item
', + "1. 1st item\n2. 2nd item", + ) + ) + test_set.append( + ( + b'
  1. 1st item
  2. 2nd item
', + "2. 1st item\n3. 2nd item", + ) + ) + test_set.append( + ( + b'
  1. 1st item
  2. 2nd item
', + "0. 1st item\n1. 2nd item", + ) + ) + test_set.append( + ( + b'
  1. 1st item
  2. 2nd item
', + "1. 1st item\n2. 2nd item", + ) + ) + test_set.append( + ( + b'
  1. 1st item
  2. 2nd item
', + "1. 1st item\n2. 2nd item", + ) + ) + + for pair in test_set: + in_doc = InputDocument( + path_or_stream=BytesIO(pair[0]), + format=InputFormat.HTML, + backend=HTMLDocumentBackend, + filename="test", + ) + backend = HTMLDocumentBackend( + in_doc=in_doc, + path_or_stream=BytesIO(pair[0]), + ) + doc: DoclingDocument = backend.convert() + assert doc + assert doc.export_to_markdown() == pair[1] + + def get_html_paths(): # Define the directory you want to search From db3ceefd4ae6251a97e333bcb03051698b3fa71a Mon Sep 17 00:00:00 2001 From: Panos Vagenas <35837085+vagenas@users.noreply.github.com> Date: Fri, 28 Feb 2025 14:54:46 +0100 Subject: [PATCH 7/7] docs: improve docs on token limit warning triggered by HybridChunker (#1077) Signed-off-by: Panos Vagenas --- docs/examples/hybrid_chunking.ipynb | 21 ++++++++++++++++++--- docs/faq.md | 15 +++++++++------ 2 files changed, 27 insertions(+), 9 deletions(-) diff --git a/docs/examples/hybrid_chunking.ipynb b/docs/examples/hybrid_chunking.ipynb index 2b7861aa..6a5f5882 100644 --- a/docs/examples/hybrid_chunking.ipynb +++ b/docs/examples/hybrid_chunking.ipynb @@ -83,7 +83,15 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Token indices sequence length is longer than the specified maximum sequence length for this model (531 > 512). Running this sequence through the model will result in indexing errors\n" + ] + } + ], "source": [ "from docling.chunking import HybridChunker\n", "\n", @@ -91,6 +99,13 @@ "chunk_iter = chunker.chunk(dl_doc=doc)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> 👉 **NOTE**: As you see above, using the `HybridChunker` can sometimes lead to a warning from the transformers library, however this is a \"false alarm\" — for details check [here](https://ds4sd.github.io/docling/faq/#hybridchunker-triggers-warning-token-indices-sequence-length-is-longer-than-the-specified-maximum-sequence-length-for-this-model)." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -337,11 +352,11 @@ "source": [ "for i, chunk in enumerate(chunks):\n", " print(f\"=== {i} ===\")\n", - " txt_tokens = len(tokenizer.tokenize(chunk.text, max_length=None))\n", + " txt_tokens = len(tokenizer.tokenize(chunk.text))\n", " print(f\"chunk.text ({txt_tokens} tokens):\\n{repr(chunk.text)}\")\n", "\n", " ser_txt = chunker.serialize(chunk=chunk)\n", - " ser_tokens = len(tokenizer.tokenize(ser_txt, max_length=None))\n", + " ser_tokens = len(tokenizer.tokenize(ser_txt))\n", " print(f\"chunker.serialize(chunk) ({ser_tokens} tokens):\\n{repr(ser_txt)}\")\n", "\n", " print()" diff --git a/docs/faq.md b/docs/faq.md index 5f54d863..ae57446f 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -150,7 +150,7 @@ This is a collection of FAQ collected from the user questions on Token indices sequence length is longer than the specified maximum sequence length for this model (530 > 512). Running this sequence through the model will result in indexing errors + > Token indices sequence length is longer than the specified maximum sequence length for this model (531 > 512). Running this sequence through the model will result in indexing errors This is a warning that is emitted by transformers, saying that actually *running this sequence through the model* will result in indexing errors, i.e. the problematic case is only if one indeed passes the particular sequence through the (embedding) model. @@ -163,14 +163,17 @@ This is a collection of FAQ collected from the user questions on max_len: - max_len = ser_tokens + ser_tokens = len(tokenizer.tokenize(ser_txt)) + if ser_tokens > chunk_max_len: + chunk_max_len = ser_tokens print(f"{i}\t{ser_tokens}\t{repr(ser_txt[:100])}...") - print(f"{max_len=}") + print(f"Longest chunk yielded: {chunk_max_len} tokens") + print(f"Model max length: {tokenizer.model_max_length}") ``` + Also see [docling#725](https://github.com/DS4SD/docling/issues/725). + Source: Issue [docling-core#119](https://github.com/DS4SD/docling-core/issues/119)