feat: enrichment steps on all convert pipelines (incl docx, html, etc) (#2251)

* allow enrichment on all convert pipelines

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* set options in CLI

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

---------

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

docling/cli/main.py

@@ -48,6 +48,7 @@ from docling.datamodel.base_models import (
 from docling.datamodel.document import ConversionResult
 from docling.datamodel.pipeline_options import (
     AsrPipelineOptions,
+    ConvertPipelineOptions,
     EasyOcrOptions,
     OcrOptions,
     PaginatedPipelineOptions,
@@ -71,8 +72,13 @@ from docling.datamodel.vlm_model_specs import (
 from docling.document_converter import (
     AudioFormatOption,
     DocumentConverter,
+    ExcelFormatOption,
     FormatOption,
+    HTMLFormatOption,
+    MarkdownFormatOption,
     PdfFormatOption,
+    PowerpointFormatOption,
+    WordFormatOption,
 )
 from docling.models.factories import get_ocr_factory
 from docling.pipeline.asr_pipeline import AsrPipeline
@@ -626,10 +632,33 @@ def convert(  # noqa: C901
                 backend=MetsGbsDocumentBackend,
             )
 
+            # SimplePipeline options
+            simple_format_option = ConvertPipelineOptions(
+                do_picture_description=enrich_picture_description,
+                do_picture_classification=enrich_picture_classes,
+            )
+            if artifacts_path is not None:
+                simple_format_option.artifacts_path = artifacts_path
+
             format_options = {
                 InputFormat.PDF: pdf_format_option,
                 InputFormat.IMAGE: pdf_format_option,
                 InputFormat.METS_GBS: mets_gbs_format_option,
+                InputFormat.DOCX: WordFormatOption(
+                    pipeline_options=simple_format_option
+                ),
+                InputFormat.PPTX: PowerpointFormatOption(
+                    pipeline_options=simple_format_option
+                ),
+                InputFormat.XLSX: ExcelFormatOption(
+                    pipeline_options=simple_format_option
+                ),
+                InputFormat.HTML: HTMLFormatOption(
+                    pipeline_options=simple_format_option
+                ),
+                InputFormat.MD: MarkdownFormatOption(
+                    pipeline_options=simple_format_option
+                ),
             }
 
         elif pipeline == ProcessingPipeline.VLM:

docling/datamodel/pipeline_options.py

@@ -259,11 +259,21 @@ class PipelineOptions(BaseOptions):
     accelerator_options: AcceleratorOptions = AcceleratorOptions()
     enable_remote_services: bool = False
     allow_external_plugins: bool = False
-
-
-class PaginatedPipelineOptions(PipelineOptions):
     artifacts_path: Optional[Union[Path, str]] = None
 
+
+class ConvertPipelineOptions(PipelineOptions):
+    """Base convert pipeline options."""
+
+    do_picture_classification: bool = False  # True: classify pictures in documents
+
+    do_picture_description: bool = False  # True: run describe pictures in documents
+    picture_description_options: PictureDescriptionBaseOptions = (
+        smolvlm_picture_description
+    )
+
+
+class PaginatedPipelineOptions(ConvertPipelineOptions):
     images_scale: float = 1.0
     generate_page_images: bool = False
     generate_picture_images: bool = False
@@ -295,13 +305,11 @@ class LayoutOptions(BaseModel):
 
 class AsrPipelineOptions(PipelineOptions):
     asr_options: Union[InlineAsrOptions] = asr_model_specs.WHISPER_TINY
-    artifacts_path: Optional[Union[Path, str]] = None
 
 
 class VlmExtractionPipelineOptions(PipelineOptions):
     """Options for extraction pipeline."""
 
-    artifacts_path: Optional[Union[Path, str]] = None
     vlm_options: Union[InlineVlmOptions] = NU_EXTRACT_2B_TRANSFORMERS
 
 
@@ -312,8 +320,6 @@ class PdfPipelineOptions(PaginatedPipelineOptions):
     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)
     )
@@ -321,9 +327,6 @@ class PdfPipelineOptions(PaginatedPipelineOptions):
 
     table_structure_options: TableStructureOptions = TableStructureOptions()
     ocr_options: OcrOptions = EasyOcrOptions()
-    picture_description_options: PictureDescriptionBaseOptions = (
-        smolvlm_picture_description
-    )
     layout_options: LayoutOptions = LayoutOptions()
 
     images_scale: float = 1.0

docling/models/base_model.py

@@ -4,7 +4,13 @@ from collections.abc import Iterable
 from typing import Any, Generic, Optional, Protocol, Type, Union
 
 import numpy as np
-from docling_core.types.doc import BoundingBox, DocItem, DoclingDocument, NodeItem
+from docling_core.types.doc import (
+    BoundingBox,
+    DocItem,
+    DoclingDocument,
+    NodeItem,
+    PictureItem,
+)
 from PIL.Image import Image
 from typing_extensions import TypeVar
 
@@ -164,8 +170,17 @@ class BaseItemAndImageEnrichmentModel(
             return None
 
         assert isinstance(element, DocItem)
-        element_prov = element.prov[0]
 
+        # Allow the case of documents without page images but embedded images (e.g. Word and HTML docs)
+        if len(element.prov) == 0 and isinstance(element, PictureItem):
+            embedded_im = element.get_image(conv_res.document)
+            if embedded_im is not None:
+                return ItemAndImageEnrichmentElement(item=element, image=embedded_im)
+            else:
+                return None
+
+        # Crop the image form the page
+        element_prov = element.prov[0]
         bbox = element_prov.bbox
         width = bbox.r - bbox.l
         height = bbox.t - bbox.b
@@ -183,4 +198,14 @@ class BaseItemAndImageEnrichmentModel(
         cropped_image = conv_res.pages[page_ix].get_image(
             scale=self.images_scale, cropbox=expanded_bbox
         )
+
+        # Allow for images being embedded without the page backend or page images
+        if cropped_image is None and isinstance(element, PictureItem):
+            embedded_im = element.get_image(conv_res.document)
+            if embedded_im is not None:
+                return ItemAndImageEnrichmentElement(item=element, image=embedded_im)
+            else:
+                return None
+
+        # Return the proper cropped image
         return ItemAndImageEnrichmentElement(item=element, image=cropped_image)

docling/pipeline/asr_pipeline.py

@@ -208,25 +208,13 @@ class AsrPipeline(BasePipeline):
 
         self.pipeline_options: AsrPipelineOptions = pipeline_options
 
-        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."
-            )
-
         if isinstance(self.pipeline_options.asr_options, InlineAsrNativeWhisperOptions):
             asr_options: InlineAsrNativeWhisperOptions = (
                 self.pipeline_options.asr_options
             )
             self._model = _NativeWhisperModel(
                 enabled=True,  # must be always enabled for this pipeline to make sense.
-                artifacts_path=artifacts_path,
+                artifacts_path=self.artifacts_path,
                 accelerator_options=pipeline_options.accelerator_options,
                 asr_options=asr_options,
             )

docling/pipeline/base_extraction_pipeline.py

@@ -1,19 +1,33 @@
 import logging
 from abc import ABC, abstractmethod
+from pathlib import Path
 from typing import Optional
 
 from docling.datamodel.base_models import ConversionStatus, ErrorItem
 from docling.datamodel.document import InputDocument
 from docling.datamodel.extraction import ExtractionResult, ExtractionTemplateType
-from docling.datamodel.pipeline_options import BaseOptions
+from docling.datamodel.pipeline_options import BaseOptions, PipelineOptions
+from docling.datamodel.settings import settings
 
 _log = logging.getLogger(__name__)
 
 
 class BaseExtractionPipeline(ABC):
-    def __init__(self, pipeline_options: BaseOptions):
+    def __init__(self, pipeline_options: PipelineOptions):
         self.pipeline_options = pipeline_options
 
+        self.artifacts_path: Optional[Path] = None
+        if pipeline_options.artifacts_path is not None:
+            self.artifacts_path = Path(pipeline_options.artifacts_path).expanduser()
+        elif settings.artifacts_path is not None:
+            self.artifacts_path = Path(settings.artifacts_path).expanduser()
+
+        if self.artifacts_path is not None and not self.artifacts_path.is_dir():
+            raise RuntimeError(
+                f"The value of {self.artifacts_path=} is not valid. "
+                "When defined, it must point to a folder containing all models required by the pipeline."
+            )
+
     def execute(
         self,
         in_doc: InputDocument,
@@ -54,5 +68,5 @@ class BaseExtractionPipeline(ABC):
 
     @classmethod
     @abstractmethod
-    def get_default_options(cls) -> BaseOptions:
+    def get_default_options(cls) -> PipelineOptions:
         pass

docling/pipeline/base_pipeline.py

@@ -4,7 +4,8 @@ import time
 import traceback
 from abc import ABC, abstractmethod
 from collections.abc import Iterable
-from typing import Any, Callable, List
+from pathlib import Path
+from typing import Any, Callable, List, Optional
 
 from docling_core.types.doc import NodeItem
 
@@ -20,9 +21,19 @@ from docling.datamodel.base_models import (
     Page,
 )
 from docling.datamodel.document import ConversionResult, InputDocument
-from docling.datamodel.pipeline_options import PdfPipelineOptions, PipelineOptions
+from docling.datamodel.pipeline_options import (
+    ConvertPipelineOptions,
+    PdfPipelineOptions,
+    PipelineOptions,
+)
 from docling.datamodel.settings import settings
 from docling.models.base_model import GenericEnrichmentModel
+from docling.models.document_picture_classifier import (
+    DocumentPictureClassifier,
+    DocumentPictureClassifierOptions,
+)
+from docling.models.factories import get_picture_description_factory
+from docling.models.picture_description_base_model import PictureDescriptionBaseModel
 from docling.utils.profiling import ProfilingScope, TimeRecorder
 from docling.utils.utils import chunkify
 
@@ -36,6 +47,18 @@ class BasePipeline(ABC):
         self.build_pipe: List[Callable] = []
         self.enrichment_pipe: List[GenericEnrichmentModel[Any]] = []
 
+        self.artifacts_path: Optional[Path] = None
+        if pipeline_options.artifacts_path is not None:
+            self.artifacts_path = Path(pipeline_options.artifacts_path).expanduser()
+        elif settings.artifacts_path is not None:
+            self.artifacts_path = Path(settings.artifacts_path).expanduser()
+
+        if self.artifacts_path is not None and not self.artifacts_path.is_dir():
+            raise RuntimeError(
+                f"The value of {self.artifacts_path=} is not valid. "
+                "When defined, it must point to a folder containing all models required by the pipeline."
+            )
+
     def execute(self, in_doc: InputDocument, raises_on_error: bool) -> ConversionResult:
         conv_res = ConversionResult(input=in_doc)
 
@@ -108,15 +131,58 @@ class BasePipeline(ABC):
     def is_backend_supported(cls, backend: AbstractDocumentBackend):
         pass
 
-    # def _apply_on_elements(self, element_batch: Iterable[NodeItem]) -> Iterable[Any]:
+
-    #    for model in self.build_pipe:
+class ConvertPipeline(BasePipeline):
-    #        element_batch = model(element_batch)
+    def __init__(self, pipeline_options: ConvertPipelineOptions):
-    #
+        super().__init__(pipeline_options)
-    #    yield from element_batch
+        self.pipeline_options: ConvertPipelineOptions
+
+        # ------ Common enrichment models working on all backends
+
+        # Picture description model
+        if (
+            picture_description_model := self._get_picture_description_model(
+                artifacts_path=self.artifacts_path
+            )
+        ) is None:
+            raise RuntimeError(
+                f"The specified picture description kind is not supported: {pipeline_options.picture_description_options.kind}."
+            )
+
+        self.enrichment_pipe = [
+            # Document Picture Classifier
+            DocumentPictureClassifier(
+                enabled=pipeline_options.do_picture_classification,
+                artifacts_path=self.artifacts_path,
+                options=DocumentPictureClassifierOptions(),
+                accelerator_options=pipeline_options.accelerator_options,
+            ),
+            # Document Picture description
+            picture_description_model,
+        ]
+
+    def _get_picture_description_model(
+        self, artifacts_path: Optional[Path] = None
+    ) -> Optional[PictureDescriptionBaseModel]:
+        factory = get_picture_description_factory(
+            allow_external_plugins=self.pipeline_options.allow_external_plugins
+        )
+        return factory.create_instance(
+            options=self.pipeline_options.picture_description_options,
+            enabled=self.pipeline_options.do_picture_description,
+            enable_remote_services=self.pipeline_options.enable_remote_services,
+            artifacts_path=artifacts_path,
+            accelerator_options=self.pipeline_options.accelerator_options,
+        )
+
+    @classmethod
+    @abstractmethod
+    def get_default_options(cls) -> ConvertPipelineOptions:
+        pass
 
 
-class PaginatedPipeline(BasePipeline):  # TODO this is a bad name.
+class PaginatedPipeline(ConvertPipeline):  # TODO this is a bad name.
-    def __init__(self, pipeline_options: PipelineOptions):
+    def __init__(self, pipeline_options: ConvertPipelineOptions):
         super().__init__(pipeline_options)
         self.keep_backend = False
 

docling/pipeline/extraction_vlm_pipeline.py

@@ -1,7 +1,6 @@
 import inspect
 import json
 import logging
-from pathlib import Path
 from typing import Optional
 
 from PIL.Image import Image
@@ -16,7 +15,10 @@ from docling.datamodel.extraction import (
     ExtractionResult,
     ExtractionTemplateType,
 )
-from docling.datamodel.pipeline_options import BaseOptions, VlmExtractionPipelineOptions
+from docling.datamodel.pipeline_options import (
+    PipelineOptions,
+    VlmExtractionPipelineOptions,
+)
 from docling.datamodel.settings import settings
 from docling.models.vlm_models_inline.nuextract_transformers_model import (
     NuExtractTransformersModel,
@@ -35,22 +37,10 @@ class ExtractionVlmPipeline(BaseExtractionPipeline):
         self.accelerator_options = pipeline_options.accelerator_options
         self.pipeline_options: VlmExtractionPipelineOptions
 
-        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."
-            )
-
         # Create VLM model instance
         self.vlm_model = NuExtractTransformersModel(
             enabled=True,
-            artifacts_path=artifacts_path,  # Will download automatically
+            artifacts_path=self.artifacts_path,  # Will download automatically
             accelerator_options=self.accelerator_options,
             vlm_options=pipeline_options.vlm_options,
         )
@@ -203,5 +193,5 @@ class ExtractionVlmPipeline(BaseExtractionPipeline):
             raise ValueError(f"Unsupported template type: {type(template)}")
 
     @classmethod
-    def get_default_options(cls) -> BaseOptions:
+    def get_default_options(cls) -> PipelineOptions:
         return VlmExtractionPipelineOptions()

docling/pipeline/simple_pipeline.py

@@ -6,21 +6,21 @@ from docling.backend.abstract_backend import (
 )
 from docling.datamodel.base_models import ConversionStatus
 from docling.datamodel.document import ConversionResult
-from docling.datamodel.pipeline_options import PipelineOptions
+from docling.datamodel.pipeline_options import ConvertPipelineOptions
-from docling.pipeline.base_pipeline import BasePipeline
+from docling.pipeline.base_pipeline import ConvertPipeline
 from docling.utils.profiling import ProfilingScope, TimeRecorder
 
 _log = logging.getLogger(__name__)
 
 
-class SimplePipeline(BasePipeline):
+class SimplePipeline(ConvertPipeline):
     """SimpleModelPipeline.
 
     This class is used at the moment for formats / backends
     which produce straight DoclingDocument output.
     """
 
-    def __init__(self, pipeline_options: PipelineOptions):
+    def __init__(self, pipeline_options: ConvertPipelineOptions):
         super().__init__(pipeline_options)
 
     def _build_document(self, conv_res: ConversionResult) -> ConversionResult:
@@ -47,8 +47,8 @@ class SimplePipeline(BasePipeline):
         return ConversionStatus.SUCCESS
 
     @classmethod
-    def get_default_options(cls) -> PipelineOptions:
+    def get_default_options(cls) -> ConvertPipelineOptions:
-        return PipelineOptions()
+        return ConvertPipelineOptions()
 
     @classmethod
     def is_backend_supported(cls, backend: AbstractDocumentBackend):

docling/pipeline/standard_pdf_pipeline.py

@@ -15,18 +15,13 @@ from docling.datamodel.pipeline_options import PdfPipelineOptions
 from docling.datamodel.settings import settings
 from docling.models.base_ocr_model import BaseOcrModel
 from docling.models.code_formula_model import CodeFormulaModel, CodeFormulaModelOptions
-from docling.models.document_picture_classifier import (
+from docling.models.factories import get_ocr_factory
-    DocumentPictureClassifier,
-    DocumentPictureClassifierOptions,
-)
-from docling.models.factories import get_ocr_factory, get_picture_description_factory
 from docling.models.layout_model import LayoutModel
 from docling.models.page_assemble_model import PageAssembleModel, PageAssembleOptions
 from docling.models.page_preprocessing_model import (
     PagePreprocessingModel,
     PagePreprocessingOptions,
 )
-from docling.models.picture_description_base_model import PictureDescriptionBaseModel
 from docling.models.readingorder_model import ReadingOrderModel, ReadingOrderOptions
 from docling.models.table_structure_model import TableStructureModel
 from docling.pipeline.base_pipeline import PaginatedPipeline
@@ -41,18 +36,6 @@ class StandardPdfPipeline(PaginatedPipeline):
         super().__init__(pipeline_options)
         self.pipeline_options: PdfPipelineOptions
 
-        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."
-            )
-
         with warnings.catch_warnings():  # deprecated generate_table_images
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             self.keep_images = (
@@ -63,7 +46,7 @@ class StandardPdfPipeline(PaginatedPipeline):
 
         self.reading_order_model = ReadingOrderModel(options=ReadingOrderOptions())
 
-        ocr_model = self.get_ocr_model(artifacts_path=artifacts_path)
+        ocr_model = self.get_ocr_model(artifacts_path=self.artifacts_path)
 
         self.build_pipe = [
             # Pre-processing
@@ -76,14 +59,14 @@ class StandardPdfPipeline(PaginatedPipeline):
             ocr_model,
             # Layout model
             LayoutModel(
-                artifacts_path=artifacts_path,
+                artifacts_path=self.artifacts_path,
                 accelerator_options=pipeline_options.accelerator_options,
                 options=pipeline_options.layout_options,
             ),
             # Table structure model
             TableStructureModel(
                 enabled=pipeline_options.do_table_structure,
-                artifacts_path=artifacts_path,
+                artifacts_path=self.artifacts_path,
                 options=pipeline_options.table_structure_options,
                 accelerator_options=pipeline_options.accelerator_options,
             ),
@@ -91,37 +74,19 @@ class StandardPdfPipeline(PaginatedPipeline):
             PageAssembleModel(options=PageAssembleOptions()),
         ]
 
-        # Picture description model
-        if (
-            picture_description_model := self.get_picture_description_model(
-                artifacts_path=artifacts_path
-            )
-        ) is None:
-            raise RuntimeError(
-                f"The specified picture description kind is not supported: {pipeline_options.picture_description_options.kind}."
-            )
-
         self.enrichment_pipe = [
             # Code Formula Enrichment Model
             CodeFormulaModel(
                 enabled=pipeline_options.do_code_enrichment
                 or pipeline_options.do_formula_enrichment,
-                artifacts_path=artifacts_path,
+                artifacts_path=self.artifacts_path,
                 options=CodeFormulaModelOptions(
                     do_code_enrichment=pipeline_options.do_code_enrichment,
                     do_formula_enrichment=pipeline_options.do_formula_enrichment,
                 ),
                 accelerator_options=pipeline_options.accelerator_options,
             ),
-            # Document Picture Classifier
+            *self.enrichment_pipe,
-            DocumentPictureClassifier(
-                enabled=pipeline_options.do_picture_classification,
-                artifacts_path=artifacts_path,
-                options=DocumentPictureClassifierOptions(),
-                accelerator_options=pipeline_options.accelerator_options,
-            ),
-            # Document Picture description
-            picture_description_model,
         ]
 
         if (
@@ -158,20 +123,6 @@ class StandardPdfPipeline(PaginatedPipeline):
             accelerator_options=self.pipeline_options.accelerator_options,
         )
 
-    def get_picture_description_model(
-        self, artifacts_path: Optional[Path] = None
-    ) -> Optional[PictureDescriptionBaseModel]:
-        factory = get_picture_description_factory(
-            allow_external_plugins=self.pipeline_options.allow_external_plugins
-        )
-        return factory.create_instance(
-            options=self.pipeline_options.picture_description_options,
-            enabled=self.pipeline_options.do_picture_description,
-            enable_remote_services=self.pipeline_options.enable_remote_services,
-            artifacts_path=artifacts_path,
-            accelerator_options=self.pipeline_options.accelerator_options,
-        )
-
     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

docling/pipeline/threaded_standard_pdf_pipeline.py

@@ -32,21 +32,16 @@ from docling.datamodel.document import ConversionResult
 from docling.datamodel.pipeline_options import ThreadedPdfPipelineOptions
 from docling.datamodel.settings import settings
 from docling.models.code_formula_model import CodeFormulaModel, CodeFormulaModelOptions
-from docling.models.document_picture_classifier import (
+from docling.models.factories import get_ocr_factory
-    DocumentPictureClassifier,
-    DocumentPictureClassifierOptions,
-)
-from docling.models.factories import get_ocr_factory, get_picture_description_factory
 from docling.models.layout_model import LayoutModel
 from docling.models.page_assemble_model import PageAssembleModel, PageAssembleOptions
 from docling.models.page_preprocessing_model import (
     PagePreprocessingModel,
     PagePreprocessingOptions,
 )
-from docling.models.picture_description_base_model import PictureDescriptionBaseModel
 from docling.models.readingorder_model import ReadingOrderModel, ReadingOrderOptions
 from docling.models.table_structure_model import TableStructureModel
-from docling.pipeline.base_pipeline import BasePipeline
+from docling.pipeline.base_pipeline import ConvertPipeline
 from docling.utils.profiling import ProfilingScope, TimeRecorder
 from docling.utils.utils import chunkify
 
@@ -294,7 +289,7 @@ class RunContext:
 # ──────────────────────────────────────────────────────────────────────────────
 
 
-class ThreadedStandardPdfPipeline(BasePipeline):
+class ThreadedStandardPdfPipeline(ConvertPipeline):
     """High-performance PDF pipeline with multi-threaded stages."""
 
     def __init__(self, pipeline_options: ThreadedPdfPipelineOptions) -> None:
@@ -310,7 +305,7 @@ class ThreadedStandardPdfPipeline(BasePipeline):
     # ────────────────────────────────────────────────────────────────────────
 
     def _init_models(self) -> None:
-        art_path = self._resolve_artifacts_path()
+        art_path = self.artifacts_path
         self.keep_images = (
             self.pipeline_options.generate_page_images
             or self.pipeline_options.generate_picture_images
@@ -337,32 +332,20 @@ class ThreadedStandardPdfPipeline(BasePipeline):
         self.reading_order_model = ReadingOrderModel(options=ReadingOrderOptions())
 
         # --- optional enrichment ------------------------------------------------
-        self.enrichment_pipe = []
+        self.enrichment_pipe = [
-        code_formula = CodeFormulaModel(
+            # Code Formula Enrichment Model
+            CodeFormulaModel(
                 enabled=self.pipeline_options.do_code_enrichment
                 or self.pipeline_options.do_formula_enrichment,
-            artifacts_path=art_path,
+                artifacts_path=self.artifacts_path,
                 options=CodeFormulaModelOptions(
                     do_code_enrichment=self.pipeline_options.do_code_enrichment,
                     do_formula_enrichment=self.pipeline_options.do_formula_enrichment,
                 ),
                 accelerator_options=self.pipeline_options.accelerator_options,
-        )
+            ),
-        if code_formula.enabled:
+            *self.enrichment_pipe,
-            self.enrichment_pipe.append(code_formula)
+        ]
-
-        picture_classifier = DocumentPictureClassifier(
-            enabled=self.pipeline_options.do_picture_classification,
-            artifacts_path=art_path,
-            options=DocumentPictureClassifierOptions(),
-            accelerator_options=self.pipeline_options.accelerator_options,
-        )
-        if picture_classifier.enabled:
-            self.enrichment_pipe.append(picture_classifier)
-
-        picture_descr = self._make_picture_description_model(art_path)
-        if picture_descr and picture_descr.enabled:
-            self.enrichment_pipe.append(picture_descr)
 
         self.keep_backend = any(
             (
@@ -374,19 +357,6 @@ class ThreadedStandardPdfPipeline(BasePipeline):
         )
 
     # ---------------------------------------------------------------- helpers
-    def _resolve_artifacts_path(self) -> Optional[Path]:
-        if self.pipeline_options.artifacts_path:
-            p = Path(self.pipeline_options.artifacts_path).expanduser()
-        elif settings.artifacts_path:
-            p = Path(settings.artifacts_path).expanduser()
-        else:
-            return None
-        if not p.is_dir():
-            raise RuntimeError(
-                f"{p} does not exist or is not a directory containing the required models"
-            )
-        return p
-
     def _make_ocr_model(self, art_path: Optional[Path]) -> Any:
         factory = get_ocr_factory(
             allow_external_plugins=self.pipeline_options.allow_external_plugins
@@ -398,20 +368,6 @@ class ThreadedStandardPdfPipeline(BasePipeline):
             accelerator_options=self.pipeline_options.accelerator_options,
         )
 
-    def _make_picture_description_model(
-        self, art_path: Optional[Path]
-    ) -> Optional[PictureDescriptionBaseModel]:
-        factory = get_picture_description_factory(
-            allow_external_plugins=self.pipeline_options.allow_external_plugins
-        )
-        return factory.create_instance(
-            options=self.pipeline_options.picture_description_options,
-            enabled=self.pipeline_options.do_picture_description,
-            enable_remote_services=self.pipeline_options.enable_remote_services,
-            artifacts_path=art_path,
-            accelerator_options=self.pipeline_options.accelerator_options,
-        )
-
     # ────────────────────────────────────────────────────────────────────────
     # Build - thread pipeline
     # ────────────────────────────────────────────────────────────────────────

docling/pipeline/vlm_pipeline.py

@@ -54,18 +54,6 @@ class VlmPipeline(PaginatedPipeline):
 
         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 = (
@@ -89,7 +77,7 @@ class VlmPipeline(PaginatedPipeline):
                 self.build_pipe = [
                     HuggingFaceMlxModel(
                         enabled=True,  # must be always enabled for this pipeline to make sense.
-                        artifacts_path=artifacts_path,
+                        artifacts_path=self.artifacts_path,
                         accelerator_options=pipeline_options.accelerator_options,
                         vlm_options=vlm_options,
                     ),
@@ -98,7 +86,7 @@ class VlmPipeline(PaginatedPipeline):
                 self.build_pipe = [
                     HuggingFaceTransformersVlmModel(
                         enabled=True,  # must be always enabled for this pipeline to make sense.
-                        artifacts_path=artifacts_path,
+                        artifacts_path=self.artifacts_path,
                         accelerator_options=pipeline_options.accelerator_options,
                         vlm_options=vlm_options,
                     ),
@@ -109,7 +97,7 @@ class VlmPipeline(PaginatedPipeline):
                 self.build_pipe = [
                     VllmVlmModel(
                         enabled=True,  # must be always enabled for this pipeline to make sense.
-                        artifacts_path=artifacts_path,
+                        artifacts_path=self.artifacts_path,
                         accelerator_options=pipeline_options.accelerator_options,
                         vlm_options=vlm_options,
                     ),

docs/examples/enrich_simple_pipeline.py

@@ -0,0 +1,35 @@
+import logging
+from pathlib import Path
+
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.pipeline_options import ConvertPipelineOptions
+from docling.document_converter import (
+    DocumentConverter,
+    HTMLFormatOption,
+    WordFormatOption,
+)
+
+_log = logging.getLogger(__name__)
+
+
+def main():
+    input_path = Path("tests/data/docx/word_sample.docx")
+
+    pipeline_options = ConvertPipelineOptions()
+    pipeline_options.do_picture_classification = True
+    pipeline_options.do_picture_description = True
+
+    doc_converter = DocumentConverter(
+        format_options={
+            InputFormat.DOCX: WordFormatOption(pipeline_options=pipeline_options),
+            InputFormat.HTML: HTMLFormatOption(pipeline_options=pipeline_options),
+        },
+    )
+
+    res = doc_converter.convert(input_path)
+
+    print(res.document.export_to_markdown())
+
+
+if __name__ == "__main__":
+    main()