Actor: Resolving conflicts with main (pass 2)

Signed-off-by: Václav Vančura <commit@vancura.dev>

README.md

@@ -23,23 +23,25 @@
 [![PyPI Downloads](https://static.pepy.tech/badge/docling/month)](https://pepy.tech/projects/docling)
 [![Docling Actor](https://apify.com/actor-badge?actor=vancura/docling?fpr=docling)](https://apify.com/vancura/docling)
 
-Docling parses documents and exports them to the desired format with ease and speed.
+Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
 
 ## Features
 
-* 🗂️ Reads popular document formats (PDF, DOCX, PPTX, XLSX, Images, HTML, AsciiDoc & Markdown) and exports to HTML, Markdown and JSON (with embedded and referenced images)
-* 📑 Advanced PDF document understanding including page layout, reading order & table structures
-* 🧩 Unified, expressive [DoclingDocument](https://ds4sd.github.io/docling/concepts/docling_document/) representation format
-* 🤖 Plug-and-play [integrations](https://ds4sd.github.io/docling/integrations/) incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
-* 🔍 OCR support for scanned PDFs
+* 🗂️ Parsing of [multiple document formats][supported_formats] incl. PDF, DOCX, XLSX, HTML, images, and more
+* 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more
+* 🧬 Unified, expressive [DoclingDocument][docling_document] representation format
+* ↪️ Various [export formats][supported_formats] and options, including Markdown, HTML, and lossless JSON
+* 🔒 Local execution capabilities for sensitive data and air-gapped environments
+* 🤖 Plug-and-play [integrations][integrations] incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
+* 🔍 Extensive OCR support for scanned PDFs and images
 * 💻 Simple and convenient CLI
 
-Explore the [documentation](https://ds4sd.github.io/docling/) to discover plenty examples and unlock the full power of Docling!
-
 ### Coming soon
 
-* ♾️ Equation & code extraction
 * 📝 Metadata extraction, including title, authors, references & language
+* 📝 Inclusion of Visual Language Models ([SmolDocling](https://huggingface.co/blog/smolervlm#smoldocling))
+* 📝 Chart understanding (Barchart, Piechart, LinePlot, etc)
+* 📝 Complex chemistry understanding (Molecular structures)
 
 ## Installation
 
@@ -143,3 +145,7 @@ For individual model usage, please refer to the model licenses found in the orig
 ## IBM ❤️ Open Source AI
 
 Docling has been brought to you by IBM.
+
+[supported_formats]: https://ds4sd.github.io/docling/usage/supported_formats/
+[docling_document]: https://ds4sd.github.io/docling/concepts/docling_document/
+[integrations]: https://ds4sd.github.io/docling/integrations/

docling/backend/docling_parse_backend.py

@@ -163,7 +163,7 @@ class DoclingParsePageBackend(PdfPageBackend):
                 l=0, r=0, t=0, b=0, coord_origin=CoordOrigin.BOTTOMLEFT
             )
         else:
-            padbox = cropbox.to_bottom_left_origin(page_size.height)
+            padbox = cropbox.to_bottom_left_origin(page_size.height).model_copy()
             padbox.r = page_size.width - padbox.r
             padbox.t = page_size.height - padbox.t
 

docling/backend/docling_parse_v2_backend.py

@@ -12,6 +12,7 @@ from pypdfium2 import PdfPage
 
 from docling.backend.pdf_backend import PdfDocumentBackend, PdfPageBackend
 from docling.datamodel.base_models import Cell, Size
+from docling.utils.locks import pypdfium2_lock
 
 if TYPE_CHECKING:
     from docling.datamodel.document import InputDocument
@@ -178,24 +179,28 @@ class DoclingParseV2PageBackend(PdfPageBackend):
                 l=0, r=0, t=0, b=0, coord_origin=CoordOrigin.BOTTOMLEFT
             )
         else:
-            padbox = cropbox.to_bottom_left_origin(page_size.height)
+            padbox = cropbox.to_bottom_left_origin(page_size.height).model_copy()
             padbox.r = page_size.width - padbox.r
             padbox.t = page_size.height - padbox.t
 
-        image = (
-            self._ppage.render(
-                scale=scale * 1.5,
-                rotation=0,  # no additional rotation
-                crop=padbox.as_tuple(),
-            )
-            .to_pil()
-            .resize(size=(round(cropbox.width * scale), round(cropbox.height * scale)))
-        )  # We resize the image from 1.5x the given scale to make it sharper.
+        with pypdfium2_lock:
+            image = (
+                self._ppage.render(
+                    scale=scale * 1.5,
+                    rotation=0,  # no additional rotation
+                    crop=padbox.as_tuple(),
+                )
+                .to_pil()
+                .resize(
+                    size=(round(cropbox.width * scale), round(cropbox.height * scale))
+                )
+            )  # We resize the image from 1.5x the given scale to make it sharper.
 
         return image
 
     def get_size(self) -> Size:
-        return Size(width=self._ppage.get_width(), height=self._ppage.get_height())
+        with pypdfium2_lock:
+            return Size(width=self._ppage.get_width(), height=self._ppage.get_height())
 
     def unload(self):
         self._ppage = None
@@ -206,23 +211,24 @@ class DoclingParseV2DocumentBackend(PdfDocumentBackend):
     def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]):
         super().__init__(in_doc, path_or_stream)
 
-        self._pdoc = pdfium.PdfDocument(self.path_or_stream)
-        self.parser = pdf_parser_v2("fatal")
+        with pypdfium2_lock:
+            self._pdoc = pdfium.PdfDocument(self.path_or_stream)
+            self.parser = pdf_parser_v2("fatal")
 
-        success = False
-        if isinstance(self.path_or_stream, BytesIO):
-            success = self.parser.load_document_from_bytesio(
-                self.document_hash, self.path_or_stream
-            )
-        elif isinstance(self.path_or_stream, Path):
-            success = self.parser.load_document(
-                self.document_hash, str(self.path_or_stream)
-            )
+            success = False
+            if isinstance(self.path_or_stream, BytesIO):
+                success = self.parser.load_document_from_bytesio(
+                    self.document_hash, self.path_or_stream
+                )
+            elif isinstance(self.path_or_stream, Path):
+                success = self.parser.load_document(
+                    self.document_hash, str(self.path_or_stream)
+                )
 
-        if not success:
-            raise RuntimeError(
-                f"docling-parse v2 could not load document {self.document_hash}."
-            )
+            if not success:
+                raise RuntimeError(
+                    f"docling-parse v2 could not load document {self.document_hash}."
+                )
 
     def page_count(self) -> int:
         # return len(self._pdoc)  # To be replaced with docling-parse API
@@ -236,9 +242,10 @@ class DoclingParseV2DocumentBackend(PdfDocumentBackend):
         return len_2
 
     def load_page(self, page_no: int) -> DoclingParseV2PageBackend:
-        return DoclingParseV2PageBackend(
-            self.parser, self.document_hash, page_no, self._pdoc[page_no]
-        )
+        with pypdfium2_lock:
+            return DoclingParseV2PageBackend(
+                self.parser, self.document_hash, page_no, self._pdoc[page_no]
+            )
 
     def is_valid(self) -> bool:
         return self.page_count() > 0
@@ -246,5 +253,6 @@ class DoclingParseV2DocumentBackend(PdfDocumentBackend):
     def unload(self):
         super().unload()
         self.parser.unload_document(self.document_hash)
-        self._pdoc.close()
-        self._pdoc = None
+        with pypdfium2_lock:
+            self._pdoc.close()
+            self._pdoc = None

docling/datamodel/pipeline_options.py

@@ -1,11 +1,20 @@
 import logging
 import os
+import re
 import warnings
 from enum import Enum
 from pathlib import Path
-from typing import Annotated, Any, Dict, List, Literal, Optional, Tuple, Type, Union
+from typing import Annotated, Any, Dict, List, Literal, Optional, Union
 
-from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+from pydantic import (
+    AnyUrl,
+    BaseModel,
+    ConfigDict,
+    Field,
+    field_validator,
+    model_validator,
+    validator,
+)
 from pydantic_settings import (
     BaseSettings,
     PydanticBaseSettingsSource,
@@ -31,7 +40,19 @@ class AcceleratorOptions(BaseSettings):
     )
 
     num_threads: int = 4
-    device: AcceleratorDevice = AcceleratorDevice.AUTO
+    device: Union[str, AcceleratorDevice] = "auto"
+    cuda_use_flash_attention2: bool = False
+
+    @field_validator("device")
+    def validate_device(cls, value):
+        # "auto", "cpu", "cuda", "mps", or "cuda:N"
+        if value in {d.value for d in AcceleratorDevice} or re.match(
+            r"^cuda(:\d+)?$", value
+        ):
+            return value
+        raise ValueError(
+            "Invalid device option. Use 'auto', 'cpu', 'mps', 'cuda', or 'cuda:N'."
+        )
 
     @model_validator(mode="before")
     @classmethod
@@ -47,7 +68,6 @@ class AcceleratorOptions(BaseSettings):
         """
         if isinstance(data, dict):
             input_num_threads = data.get("num_threads")
-
             # Check if to set the num_threads from the alternative envvar
             if input_num_threads is None:
                 docling_num_threads = os.getenv("DOCLING_NUM_THREADS")
@@ -79,7 +99,7 @@ class TableStructureOptions(BaseModel):
         #        are merged across table columns.
         # False: Let table structure model define the text cells, ignore PDF cells.
     )
-    mode: TableFormerMode = TableFormerMode.FAST
+    mode: TableFormerMode = TableFormerMode.ACCURATE
 
 
 class OcrOptions(BaseModel):
@@ -125,6 +145,7 @@ class RapidOcrOptions(OcrOptions):
     det_model_path: Optional[str] = None  # same default as rapidocr
     cls_model_path: Optional[str] = None  # same default as rapidocr
     rec_model_path: Optional[str] = None  # same default as rapidocr
+    rec_keys_path: Optional[str] = None  # same default as rapidocr
 
     model_config = ConfigDict(
         extra="forbid",
@@ -189,6 +210,90 @@ class OcrMacOptions(OcrOptions):
     )
 
 
+class PictureDescriptionBaseOptions(BaseModel):
+    kind: str
+    batch_size: int = 8
+    scale: float = 2
+
+    bitmap_area_threshold: float = (
+        0.2  # percentage of the area for a bitmap to processed with the models
+    )
+
+
+class PictureDescriptionApiOptions(PictureDescriptionBaseOptions):
+    kind: Literal["api"] = "api"
+
+    url: AnyUrl = AnyUrl("http://localhost:8000/v1/chat/completions")
+    headers: Dict[str, str] = {}
+    params: Dict[str, Any] = {}
+    timeout: float = 20
+
+    prompt: str = "Describe this image in a few sentences."
+    provenance: str = ""
+
+
+class PictureDescriptionVlmOptions(PictureDescriptionBaseOptions):
+    kind: Literal["vlm"] = "vlm"
+
+    repo_id: str
+    prompt: str = "Describe this image in a few sentences."
+    # Config from here https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.GenerationConfig
+    generation_config: Dict[str, Any] = dict(max_new_tokens=200, do_sample=False)
+
+    @property
+    def repo_cache_folder(self) -> str:
+        return self.repo_id.replace("/", "--")
+
+
+smolvlm_picture_description = PictureDescriptionVlmOptions(
+    repo_id="HuggingFaceTB/SmolVLM-256M-Instruct"
+)
+# phi_picture_description = PictureDescriptionVlmOptions(repo_id="microsoft/Phi-3-vision-128k-instruct")
+granite_picture_description = PictureDescriptionVlmOptions(
+    repo_id="ibm-granite/granite-vision-3.1-2b-preview",
+    prompt="What is shown in this image?",
+)
+
+
+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."""
@@ -217,14 +322,40 @@ class PipelineOptions(BaseModel):
     )
     document_timeout: Optional[float] = None
     accelerator_options: AcceleratorOptions = AcceleratorOptions()
+    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
     do_table_structure: bool = True  # True: perform table structure extraction
     do_ocr: bool = True  # True: perform OCR, replace programmatic PDF text
+    do_code_enrichment: bool = False  # True: perform code OCR
+    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[
@@ -234,6 +365,10 @@ class PdfPipelineOptions(PipelineOptions):
         OcrMacOptions,
         RapidOcrOptions,
     ] = Field(EasyOcrOptions(), discriminator="kind")
+    picture_description_options: Annotated[
+        Union[PictureDescriptionApiOptions, PictureDescriptionVlmOptions],
+        Field(discriminator="kind"),
+    ] = smolvlm_picture_description
 
     images_scale: float = 1.0
     generate_page_images: bool = False