Merge remote-tracking branch 'origin/main' into fix-threadsafe-pypdfium

.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

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

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 <tr> elements)

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[
@@ -43,18 +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(
@@ -65,6 +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(
@@ -73,7 +96,7 @@ 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 ([m for m in _AvailableModels] if all else _default_models)
     output_dir = download_models(
         output_dir=output_dir,
         force=force,
@@ -83,6 +106,7 @@ 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,
     )
 

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]

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[

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

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"

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 <loc_...> bounding box coords from the chunk, normalized by / 500."""
+            coords = re.findall(r"<loc_(\d+)>", 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. "<otsl>", "<loc_140>", etc.)
+            tokens = re.findall(pattern, s)
+            # Remove any tokens that start with "<loc_"
+            tokens = [
+                token
+                for token in tokens
+                if not (
+                    token.startswith(rf"<{DocumentToken.LOC.value}")
+                    or token
+                    in [
+                        rf"<{DocumentToken.OTSL.value}>",
+                        rf"</{DocumentToken.OTSL.value}>",
+                    ]
+                )
+            ]
+            # 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"</{DocumentToken.OTSL.value}>",
+                    ]
+                )
+            ]
+            # 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 <tag>...</tag> 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<tag>{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})>.*?</(?P=tag)>"
+            )
+
+            # 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)

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(

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)),

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.

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()"

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("================================================")

docs/faq.md

@@ -132,9 +132,48 @@ This is a collection of FAQ collected from the user questions on <https://github
     ```
 
 
-??? Some images are missing from MS Word and Powerpoint"
+??? question "Some images are missing from MS Word and Powerpoint"
 
     ### Some images are missing from MS Word and Powerpoint
 
     The image processing library used by Docling is able to handle embedded WMF images only on Windows platform.
     If you are on other operaring systems, these images will be ignored.
+
+
+??? question "`HybridChunker` triggers warning: 'Token indices sequence length is longer than the specified maximum sequence length for this model'"
+
+    ### `HybridChunker` triggers warning: 'Token indices sequence length is longer than the specified maximum sequence length for this model'
+
+    **TLDR**:
+    In the context of the `HybridChunker`, this is a known & ancitipated "false alarm".
+
+    **Details**:
+
+    Using the [`HybridChunker`](./concepts/chunking.md#hybrid-chunker) often triggers a warning like this:
+    > 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.
+
+    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
+    chunk_max_len = 0
+    for i, chunk in enumerate(chunks):
+        ser_txt = chunker.serialize(chunk=chunk)
+        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"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)

poetry.lock

@@ -1,4 +1,35 @@
-# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
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 [package.dependencies]
@@ -773,13 +804,13 @@ files = [
 
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 description = "Decorators for Humans"
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 [[package]]
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 [[package]]
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 ]
 
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 [[package]]
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 python-versions = ">=3.9"
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 [[package]]
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 ]
 
 [[package]]
@@ -6166,13 +6213,13 @@ train = ["accelerate (>=0.20.3)", "datasets"]
 
 [[package]]
 name = "setuptools"
-version = "75.8.0"
+version = "75.8.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.9"
 files = [
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+    {file = "setuptools-75.8.1.tar.gz", hash = "sha256:65fb779a8f28895242923582eadca2337285f0891c2c9e160754df917c3d2530"},
 ]
 
 [package.extras]
@@ -7781,9 +7828,9 @@ type = ["pytest-mypy"]
 ocrmac = ["ocrmac"]
 rapidocr = ["onnxruntime", "onnxruntime", "rapidocr-onnxruntime"]
 tesserocr = ["tesserocr"]
-vlm = ["transformers", "transformers"]
+vlm = ["accelerate", "transformers", "transformers"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9"
-content-hash = "21c25b86d88aa138f7faa68fcba95af1d9a5edaa22550bd325997aed6eef44fe"
+content-hash = "1d4718b694098b0676f1ad1606d769887e51fc29f604e5f4c83dd5e1c90557e7"

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 <cau@zurich.ibm.com>", "Michele Dolfi <dol@zurich.ibm.com>", "Maxim Lysak <mly@zurich.ibm.com>", "Nikos Livathinos <nli@zurich.ibm.com>", "Ahmed Nassar <ahn@zurich.ibm.com>", "Panos Vagenas <pva@zurich.ibm.com>", "Peter Staar <taa@zurich.ibm.com>"]
 license = "MIT"
@@ -58,10 +58,14 @@ onnxruntime = [
   { version = ">=1.7.0,<1.20.0", optional = true, markers = "python_version < '3.10'" },
   { version = "^1.7.0", optional = true, markers = "python_version >= '3.10'" }
 ]
+
 transformers = [
   {markers = "sys_platform != 'darwin' or platform_machine != 'x86_64'", version = "^4.46.0", optional = true },
   {markers = "sys_platform == 'darwin' and platform_machine == 'x86_64'", version = "~4.42.0", optional = true }
 ]
+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]

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"<html><body><ol><li>1st item</li><li>2nd item</li></ol></body></html>",
+            "1. 1st item\n2. 2nd item",
+        )
+    )
+    test_set.append(
+        (
+            b'<html><body><ol start="1"><li>1st item</li><li>2nd item</li></ol></body></html>',
+            "1. 1st item\n2. 2nd item",
+        )
+    )
+    test_set.append(
+        (
+            b'<html><body><ol start="2"><li>1st item</li><li>2nd item</li></ol></body></html>',
+            "2. 1st item\n3. 2nd item",
+        )
+    )
+    test_set.append(
+        (
+            b'<html><body><ol start="0"><li>1st item</li><li>2nd item</li></ol></body></html>',
+            "0. 1st item\n1. 2nd item",
+        )
+    )
+    test_set.append(
+        (
+            b'<html><body><ol start="-5"><li>1st item</li><li>2nd item</li></ol></body></html>',
+            "1. 1st item\n2. 2nd item",
+        )
+    )
+    test_set.append(
+        (
+            b'<html><body><ol start="foo"><li>1st item</li><li>2nd item</li></ol></body></html>',
+            "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